=Paper=
{{Paper
|id=Vol-2871/paper1
|storemode=property
|title=Research on Heterogeneous Enhanced Network Embedding for Cooperation Prediction
|pdfUrl=https://ceur-ws.org/Vol-2871/paper1.pdf
|volume=Vol-2871
|authors=Xin Zhang,Yi Wen,Haiyun Xu
|dblpUrl=https://dblp.org/rec/conf/iconference/ZhangWX21
}}
==Research on Heterogeneous Enhanced Network Embedding for Cooperation Prediction==
https://ceur-ws.org/Vol-2871/paper1.pdf
1st Workshop on AI + Informetrics - AII2021
Research on Heterogeneous Enhanced Network
Embedding for Collaboration Prediction
Xin Zhang1[0000-0001-8784-3788] ,Yi Wen1[0000-0002-6520-2733] and Haiyun Xu2[0000-0002-7453-3331]
1
Chengdu Library and Information Center,CAS, Chengdu 610041, Sichuan, China
2
Business School, Shandong University of Technology ,Zibo 255049,Shandong, China
1
zhangxin@clas.ac.cn
Abstract. Scientific research collaboration has always been an important
research content of information science, and collaboration prediction is an
important issue in personalized information services. This article constructs an
author-centered heterogeneous information fusion schema, and uses causal
analysis to quantitatively study the influence of same institutions, co-word and
citation on collaboration, compares the effects of different network embedding
algorithms in collaboration prediction, and built heterogeneous information
fused network embedding model for collaboration prediction. Take the field of
stem cells as an empirical case, experiments show that the matrix
decomposition based network embedding algorithms(like NetMF) are balance
of performance and accuracy. Institutions, keywords and citations can improve
the prediction effect of collaboration, and (same institutions > citations > co
-words) among them. Multiple features fusion models are generally better than
single information fusion, and the model of (collaboration + same institution +
citations) performs outstanding in collaboration prediction.
Keywords: collaboration Prediction , Network embedding, Heterogeneous
information.
1 Introduction
Since the birth of information science, scientific research collaboration has
become an important research topic. The prediction of collaboration has important
theoretical and practical significance for the analysis of S&T trends and the
recommendation of personalized service information. Collaboration prediction is also
a very challenging task. Scholars in different fields have invested in this topic. Most
computer scientists design more and more advanced and complex network
representation learning algorithms, and find ways to incorporate different types of
information such as text into representation learning. Intelligence experts pay more
attention to the application of these algorithms for collaborative recommendation
Newman[1] was the first to introduce the network analysis into research
collaboration. Liben-Nowell and Kleinberg [2] put forward the problem of link
prediction in social networks, they gave some similarity measurements based on
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
2
network structure, and applied two series of index methods based on nodes and paths,
empirical analysis is carried out in the collaboration network of authors in five fields
of physics. Since then, quit a lot of work focused on improve the indicators in this
article. In the book "Link Prediction", Lv Linyuan introduced a series of
similarity-based link prediction indicators, such as common neighbor index (CN),
cosine similarity, Jaccard Indicators, Adamic-Adar (AA) indicators, resource
allocation (RA) indicators, LHN-I indicators and others based on the local
information of nodes, as well as local path indicators (LP), Katz indicators, restart
random walks, etc. based on the information of edges or paths[3]. Intelligence
experts has conducted a series of empirical studies based on these indicators, such as
R Guns and R Rousseau [4] ,they constructed a weighted collaboration network in the
field of malaria and tuberculosis to carry out cooperative prediction and
recommendation work. Yan E et al. [5] used the papers of 59 journals in the field of
library and information as an empirical case to compare the prediction results under
various indicators. Shan Songyan et al. [6] summarized and reviewed the author
similarity algorithm for cooperative prediction. In addition, cooperative prediction
also has methods based on maximum likelihood estimation, methods based on
probability graph models, etc. The latter two approaches are based on statistical ideas
and have achieved certain effects on specific networks. The computational complexity
of those method is too high, and it is difficult to implement on large-scale networks.
In response to the large-scale sparse network prediction problems encountered in
actual research, researchers continue to propose new methods to learn a
low-dimensional dense representation of the network. Then use low-dimensional
representation for structural prediction work. For example, Zhang Jinzhu et al[7]
introduced the method of network representation learning into the collaboration
prediction. In this paper, the LINE model is used to construct the vector
representation of author, and the cosine similarity is used to measure the possibility of
collaboration. Yu Chuanming et al[8] studied the application of network
representation learning methods in collaborative recommendation, proposed an
integrated recommendation model, and conducted empirical analysis in the financial
field.
Moreover, it must be noted that collaboration is affected by many complicated
factors. In addition to the network structure, collaboration prediction should also use
rich heterogeneous structure information. Wang Zhibing et al. [9] merged the attribute
characteristics such as the node mechanism into the similarity index of the network
structure, and carried out collaboration prediction. Liu Ping et al. [10] constructed an
LDA-based author interest model based on the community division, and then
analyzed the author’s relevant literature to achieve the purpose of scientific research
collaboration recommendation; Yu Chuanming et al. [11] constructed the
collaboration network in the financial field and adopted a link prediction method
based on feature fusion of individuals, institutions and regions. Lin Yuan et al. [12]
combined the heterogeneous information of scholars, institutions, and keywords to
construct a scientific research collaboration network, and then used node2vec to
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express learning methods for cooperative prediction. Zhang Xin et al. [13] proposed a
scientific research collaboration prediction method that combines network
representation learning and author's topic characteristics .On the basis of these studies,
this paper constructs a cooperative prediction method of feature fusion.
2 Ideas and Methods
2.1 Research Framework
Fig. 1. Research Framework of this paper
The research framework of this paper is shown in Figure 1. It can be roughly
divided into 6 stages, namely (1) data collection -> (2) exploratory data analysis and
network construction -> (3) the impact of heterogeneous information on scientific
research collaboration -> (4) embedding algorithms evaluation for link prediction ->
(5)
The construction of heterogeneous features fused network embedding approaches and
(6) Results and analysis. Retrieve the documents to be analyzed from the Web of
Sciences database, and then conduct exploratory analysis on the collaboration
relationship among them, and construct a collaboration network, author-institution
network, author-keyword network, author-citation network. Later, for the same
institutions, shared keywords and citation relationships, the framework of causal
analysis was used to study their impact on collaboration. Then, we discuss the
performance and efficiency of DeepWalk, LINE, HOPE, Node2vec, SDNE, NetMF
and ProNE and other models for collaboration predictions. Finally, we integrate the
Institution(I), Keyword(K) and Citation(C) features to network embedding models
and use these schema to carry out actual network prediction task.
2.2 Research on Heterogeneous Information and Its Impact on collaboration
Heterogeneous information fusion is abbreviated as data fusion, which refers to
the comprehensive analysis of different types of information sources or relational data
through a specific method, and finally all the information can be used to jointly reveal
the characteristics of the research object, making up for the single data type and the
single relation type to reveal the research field insufficient associations between
4
entities to obtain more comprehensive and objective measurement results (Xu Haiyun
et al. [14]).
Hua Berlin [15] put forward the fusion theory as one of the main methodologies
of information science , and emphasized the important of data fusion, information
fusion and knowledge fusion in information science. Later, he further discussed the
influence of data fusion on intelligence. The importance of fusion of different types of
multiple source information is analyzed. He also systematically explained the relevant
theories and applications of information fusion from the perspective of information
fusion's representation process, technical algorithms and models. Morris et al. [16]
gave an overview of common measurement entities in scientific and technological
literature, mainly including documents themselves, references, journals where
documents are located, authors of documents, journals where references are located,
authors of reference documents, subject headings etc. Xu Haiyun et al. [14] reviewed
the multiple source data fusion methods in Scientometrics. The document-centered
data fusion meta-path model given in the paper is shown in Figure 2(a). This article
takes the author's collaboration as the research object, and studies the influence of the
same institution, the same keywords, and the citation relationship on scientific
research collaboration. On the basis of Figure 2(a), an author-centered scientific
research collaboration network is constructed, as shown in Figure 2(b).
(a) Document-centered schema (b) Author-centered schema
Fig. 2. Heterogeneous Information Fusion Schema
Table. 1. Heterogeneous Information Network Construction
Information Networks directly extract from Uniform Network
documents
collaboratio collaboration Network collaboration Network
n
Institution Author-Institution Bipartite Network Same Institution Network
Keyword Author-Document Bipartite Network Author Co-word Network
Document-Keyword Bipartite Network
5
Citation Author-Document Bipartite Network Author Citation Network
Document Citation Network
Table 1 shows several methods for constructing heterogeneous network
information. The first column represents the fused heterogeneous information, the
second column represents the feature network that can be directly extracted from the
article, and the third column represents the mapping of the second type of network to
the author dimension The formed network.
(1) Author collaboration network. The author collaboration network can be
extracted directly from the author field of the article. The nodes in the network
represent authors, the edges in the network represent collaboration relationships, and
the weight of the edges represents the frequency of collaboration.
(2) The same institution network, extract the author institution bipartite network
from the author-institution (C1) field of the article. An institution can contain multiple
authors, and at the same time, an author can work part-time in multiple institutions,
which can be mapped into Authors are in the same organization network. The nodes
in the network represent authors. If two authors have the same organization to which
they belong, the network is connected.
(3) The author co-word network, according to the downloaded documents, we
can construct an article-author network, article-keyword network, and then form an
author-keyword bipartite network. The weight d(a,k) in the network indicates that the
the frequency of author a uses keywords k (number of articles), (a ) represents the
neighbor node of node a, that is, the keyword used by a. According to this network,
we can deduce the author co-word network. The nodes in this network represent the
author. The edge weight w(a,b) between author a and b is calculated by formula (1),
D represents the total number of documents, and d(k) represents the frequency of
keyword k.
D
w ( a, b)
k ( a ) ( b )
min( d (a, k ), d (b, k )) log
d (k )
(1)
(4) Author citation network, based on the downloaded documents, an
author-article bipartite network can be constructed (the article written by author a in
this network is denoted as (a ) ), an article citation network, and then an author’s
citation network is formed. The weight c(a,b) in this network is the number of articles
of author b cited in all articles of author a.
c ( a, b) (d , c) (2)
d ( a ) c ( b )
if document d cite document c， ( d , c ) 1 ，Otherwise ( d , c ) 0 .Unlike the
previous two networks, the author cited network is a directed network.
In this way, we have constructed several heterogeneous information networks.
However, the influence of different heterogeneous information on collaboration may
be different. In order to measure the difference of this influence, we adopt the
paradigm of causal reasoning. ACE (Average Casual Effect) is used to measure the
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influence of treatment variable Y (same institution, co-word, citation) to variable X
(collaboration).
ACE (Y X ) E ( X | Y ) E ( X |~ Y ) (3)
The larger the ACE, the more obvious the causal effect of Y on X.
2.3 Network Embedding Algorithms for Cooperation Prediction and
Evaluation Criteria
Network embedding is currently a hot method in the field of cooperative
prediction. Scholars continue to incorporate new ideas and methods into network
representation learning, and the accuracy and efficiency of the algorithm continue to
improve. This article compares seven well-known and commonly used network
representation learning algorithms and evaluates their accuracy and performance in
scientific research collaboration prediction.
Given a network G(V,E), V is the set of vertices, E is the set of edges,
|V|=N,|E|=M, the adjacency matrix of the graph is W.
(1) DeepWalk, influenced by the well-known word2vec, Perozzi et al. proposed
the DeepWalk [17] in KDD2014. The model uses random walks to generate vertex
sequences, and each node sequence is analogous to a sentence in the language. Each
node pair is analogous to a word, and the Skip-gram method is used for learning and
training, and the vector representation of the node is obtained.
(2) LINE (Tang Jian et al., 2015) [18] defines the first-order similarity
relationship (1st order proximity) and the second-order proximity relationship (2nd
order proximity) in the graph in the algorithm. The distance is closer. The algorithm
defines first-order and second-order optimization goals to characterize this
relationship.
(3) Node2vec is a work published on KDD2016 by Grover A et al. [19]. They
improved the random walk strategy in the DeepWalk algorithm, and also considered
the direct neighbor relationship (homophily) and structural similarity relationship of
the node. Using depth-first and breadth-first node traversal, a random walk method is
designed. Assuming a random walk sequence from node u to node v, then for each v's
neighbor node w, the next step is selected according to probability sampling with
parameters p and q.
(4) SDNE (Wang D et al., 2016) [20], also published on KDD2016, SDNE
model can be seen as an extension of LINE model, the essence of the method is a
Graph AutoEncoder, making graph representation The reconstruction loss of is as
small as possible, and the vector representation of nodes with connected edges is as
close as possible.
(5) The HOPE [21] method depicts two different representations for each node,
and focuses on preserving the asymmetry information in the original network.
Different asymmetric relationship matrices are constructed, and then the JDGSVD
algorithm is used for matrix reduction. Dimension gets the network representation of
the node.
(6) NetMF, Tang J team unified deepwalk, line, node2vec and other algorithms
into the framework of matrix decomposition, and proposed a NetMf representation
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learning method that directly decomposes the target network [22]. After that, they
performed the algorithm again. Improved, introduced sparse matrix factorization, and
proposed NetSMF [23].
(7) ProNE, Jie Zhang from the team of Professor Tang Jie of Tsinghua
University proposed a fast and scalable large-scale network representation learning
algorithm ProNE[24] at IJCAI 2019. The algorithm is divided into two steps.
1)Sparse matrix factorization for fast embedding initialization and 2) Spectral
propagation in the modulated networks for embedding enhancement. Compared with
the classical random walk method, this method has an efficiency improvement of tens
to hundreds of times.
The network embedding approaches and graph neural networks are moving from
theory to application. In 2018, the Alimama team open sourced Euler, a distributed
graph deep learning tool, DeepMind open sourced the graph_nets graph network tool,
New York University researchers open sourced the graph neural network learning
framework DGL, and in 2019 Facebook open sourced PyTorch-based graph
representation learning and neural The network framework PyTorch-BigGraph [25].
In addition, recent graph representation learning tools include Tsinghua University’s
data mining team’s CogDL[26] and Tang Jian’s graph representation learning system
GraphVite[27]. The emergence of these platform tools has lowered the application
threshold of graph representation learning, allowing graph representation learning to
be applied in a wider range.
In this paper, we use those network embedding for collaboration prediction. The
collaboration prediction problem is transformed into a binary classification problem
with/without links. According to the specific ratio, the edge set is divided into training
set and test set respectively, then taking the training set as the baseline, we take the
edges in the test set as positive samples, randomly generate the same number of
negative samples to evaluate the model. We use the AUC, ROC_AUC and F1_Score
indicators to evaluate the accuracy of the model. The AUC indicator is the most
commonly used indicator to evaluate the link prediction problem. Provided the rank of
all non-observed links, the AUC value can be interpreted as the probability that a
randomly chosen missing link is given a higher score than a randomly chosen
nonexistent link [28]. The AUC value ranges from 0.5 to 1. The AUC value of random
allocation method is 0.5, and the AUC value of perfect prediction is 1. The closer the
AUC value is to 1, the better the model effect is, and the ROC_AUC is defined as the
area under the ROC curve in the binary classification problem, while the F1_Score is
the balance of precision and recall in the binary classification problem. Moreover, we
use execution time to measure the efficiency of the model.
2.4 Multiple Feature Fused collaboration Prediction Model
The method of fusing multiple features is shown in Figure 3. It extracts the basic
collaboration relationship, the same organization relationship, the co-word
relationship of articles, and the citation relationship between authors from the
document collection, and constructs the collaboration network, the same organization
network, the co-word network, and the citation network.
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Fig. 3. Algorithm Flow
In this part, this article discusses the influence of several different feature
combination methods on the prediction results of scientific research collaboration.
The fusion features in the article can be divided into two types: single feature fusion
and multiple feature fusion methods. Feature fusion is the result of fusing multiple
sets of features, based on the analysis of the experimental results in the previous part.
The same institution, the same keywords, and the citation relationship all have a
positive relationship to collaboration. This section discusses the influence of several
characteristics and their combinations on the performance of scientific research
collaboration predictions. In this section, we also use the AUC, ROC_AUC and
F1_Score indicators to evaluate the model.
Table. 2. Various feature fusion methods
category abbr. description
Baseline Raw Raw Network
Single Raw+I(ri) Raw Network+Same Institution Network
feature Raw+K(rk) Raw Network+Author Coword Network
Raw+C(rc) Raw Network+Author Citation Network
Multiple Raw+I+K(rik) Raw Network+ Same Institution Network+ Author
feature Citation Network
Raw+K+C(rkc) Raw Network+ Author Coword Network+
Author Citation Network
Raw+I+C(ric) Raw Network+ Same Institution Network+
Author Citation Network
Raw+I+K+C(rikc) Raw Network+ Same Institution Network+
Author Coword Network+ Author Citation Network
3 Experiments
3.1 Field selection and exploratory analysis
Research field selection
Stem cell and regenerative medicine research has brought revolutionary changes
to the treatment of cancer and other diseases. It has been selected as one of the top ten
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scientific and technological advances in the US "Science" magazine for 9 times. The
project has also laid out related projects many times, so the author selects the field of
stem cells for empirical research. Search in ISI Web of Knowledge with the search
formula (TI=Stem Cells). The search time was May 2019. The search yielded 433 469
articles. The number of articles in different years in the search results is shown in
Figure 4, which shows that the number of articles is presented The trend of slow
growth to rapid growth and then to saturation growth may have declined in 2019 due
to incomplete data collection.
Fig. 4. Number of articles per year
Heterogeneous information extraction
Analyzed from the ISI database, DE author keywords, C1 field represents the
author institution, the citation relationship between articles is obtained from the CR
field, and the author and institution information is split from the author institution
field. The institution information only intercepts information such as university
hospitals.
After splitting, 1,682,654 authors were obtained, and 1,461,721 authors were
merged. More than 40,000 authors were selected as the first author to publish articles.
Considering calculation performance, we selected 5,403 of these authors who have
collaborated with other authors more than 2 times. The total number of collaboration
between these authors is 4,818, forming Table 3 shows the basic statistical
characteristics of the benchmark cooperative network by year.
Table. 3. Annual statistical characteristics of the collaboration network
Year #Nodes #Edges Density
2007 65 43 2.0673E-02
2008 402 296 3.6724E-03
2009 529 367 2.6279E-03
2010 705 486 1.9584E-03
10
2011 844 593 1.6669E-03
2012 1044 723 1.3280E-03
2013 1255 878 1.1158E-03
2014 1355 960 1.0465E-03
2015 1515 1136 9.9053E-04
2016 1384 969 1.0125E-03
2017 1259 847 1.0696E-03
2018 1052 745 1.3476E-03
2019 392 255 3.3274E-03
The predictability of collaboration networks
Predictability is an important research problem in link prediction. The
predictability of the network represents the upper limit of prediction. Random
networks are completely unpredictable. Any link prediction algorithm will not get
better results on completely random networks. The predictability of the network is
related to the characteristics and evolution of the network itself. Two articles by
Newman et al [29,30] studied the structural path and other properties of the scientific
research collaboration network. Xu Xiaoke [31], Tan Suoyi [32] and others have
conducted special research on the predictability of the network. Studies have shown
that in networks with good predictability, the largest eigenvalue of the adjacency
matrix of the network is much larger than the second largest eigenvalue.
Fig.5. Eigenvalue distribution of adjacency matrix
Figure 6 shows the eigenvalue distribution of the critical matrix of the studied
network. The largest eigenvalue is about 23.37. There are obvious gaps on the right
and left sides of the figure, and the network will have better predictability.
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3.2 The impact of features on collaboration from the perspective of causal
In this part, we use the method introduced in section 2.2 to compare the effects
of the same institution, co-word, and citation on the probability of collaboration by
year.
Institutional Information
First, we studied whether the same institution has a causal effect on scientific
research collaboration in a large network composed of the first authors of all
cooperative articles, and calculated separately the same institution-there is
collaboration, the same institution-no collaboration, and different institutions-the side
of collaboration. The number, and then subtract the first few numbers from all
possible edges to get the frequency of different organizations-no collaboration. The
results are shown in Table 4, suppose that event X: there is collaboration between
authors, and Y: the authors have the same organization.
Table. 4. Same Institution and its impact of collaboration in the big network
X ~X
Y 16205 1908127
~Y 24937 984907682
P(X|Y)=16205/(16205+1908127)=8.421e-3
P(X|~Y)=24937/(24937+984907682)=2.5318e-5
The average causal effect of event Y on event X:
ACE(Y—>X)=P(X|Y)-P(X|~Y)=8.396e-3
The probability of collaboration is increased (PI) by 331.62.
Next, we disassemble the collaboration network of 5403 nodes and 4818 edges
according to the year to discuss the causal effect of scientific research collaboration
with the institution each year. The results are shown in Table 5.
Table.5. The impact of the same institution on collaboration by year
Year X&Y X &~Y ~X& Y ~X&~Y P(X|~Y) P(X|Y) PI
2007 12 21 4 2043 1.0174E-02 7.5000E-01 72.71
2008 79 139 240 80143 1.7314E-03 2.4765E-01 142.03
2009 116 159 473 138908 1.1433E-03 1.9694E-01 171.25
2010 171 192 1030 246767 7.7746E-04 1.4238E-01 182.14
2011 197 265 1318 353966 7.4810E-04 1.3003E-01 172.82
2012 246 310 1667 542223 5.7139E-04 1.2859E-01 224.05
2013 287 397 2177 784024 5.0611E-04 1.1648E-01 229.14
2014 321 421 2559 914034 4.6038E-04 1.1146E-01 241.10
2015 341 557 2515 1143442 4.8689E-04 1.1940E-01 244.23
2016 332 409 2091 954204 4.2845E-04 1.3702E-01 318.81
2017 288 355 1622 789646 4.4937E-04 1.5079E-01 334.55
2018 247 310 1251 551018 5.6228E-04 1.6489E-01 292.25
2019 80 116 116 76324 1.5175E-03 4.0816E-01 267.97
It can be seen from Figure 5 that for each time slice of the collaboration network,
the same institution has a very obvious causal effect on scientific research
12
collaboration, and the probability of scientific research collaboration with the same
institution is increased by 200-300 times.
Keyword information
This part discusses whether the authors have common keywords that affect
scientific research collaboration. Let event X: there is collaboration between authors,
and Y: there are common keywords used by authors. The method similar to section
3.2.1 is used for annual calculation. There is a causal effect of common use of
keywords on scientific research collaboration. The results are shown in Table 6.
Table. 6. The impact of the co-word on collaboration by year
Year X&Y X &~Y ~X& Y ~X&~Y P(X|~Y) P(X|Y) PI
2007 0 33 0 2047 1.5865E-02 0 -1
2008 1 217 8 80375 2.6926E-03 1.1111E-01 40.27
2009 34 241 1052 138329 1.7392E-03 3.1308E-02 17.00
2010 95 268 3727 244070 1.0968E-03 2.4856E-02 21.66
2011 139 323 8327 346957 9.3009E-04 1.6419E-02 16.65
2012 189 367 20044 523846 7.0010E-04 9.3412E-03 12.34
2013 228 456 26193 760008 5.9963E-04 8.6295E-03 13.39
2014 290 452 42131 874462 5.1662E-04 6.8362E-03 12.23
2015 352 546 58446 1087511 5.0181E-04 5.9866E-03 10.93
2016 341 400 53734 902561 4.4299E-04 6.3061E-03 13.24
2017 313 330 58653 732615 4.5024E-04 5.3081E-03 10.79
2018 349 208 58258 494011 4.2087E-04 5.9549E-03 13.15
2019 138 58 9827 66613 8.6994E-04 1.3848E-02 14.92
As can be seen from Table 6, for each time slice of the collaboration network,
the use of the same keyword has a causal effect on collaboration, and the co-word
increases the probability of collaboration by about 10-20 times. Not as obvious as the
causal effect of the same organization. This may also be due to the fact that the
authors tend to use some common keywords in the small areas of our research. These
common keywords that are used too frequently have weakened the impact of keyword
co-occurrence on collaboration.
Reference information
This part discusses the citations on research collaboration. Let event X: there is
collaboration between authors and Y: there is citation between authors,that is author a
cite some documents of author b or author b cite some documents of author a. Using
the method similar to section 3.2.1, the causal effect of citations on scientific research
collaboration is calculated annually. . The results are shown in Table 7.
Table. 7. The impact of the citations on collaboration by year
Year X&Y X &~Y ~X& Y ~X^~Y P(X|~Y) P(X|Y) PI
2007 0 33 0 2047 1.5865E-02 0 -1
2008 0 218 0 80383 2.7047E-03 0 -1
2009 3 272 32 139349 1.9481E-03 8.5714E-02 43.00
2010 44 319 228 247569 1.2869E-03 1.6176E-01 124.70
2011 115 347 825 354459 9.7800E-04 1.2234E-01 124.09
13
2012 145 411 1537 542353 7.5724E-04 8.6207E-02 112.84
2013 213 471 2518 783683 6.0065E-04 7.7993E-02 128.85
2014 259 483 4132 912461 5.2906E-04 5.8984E-02 110.49
2015 312 586 5165 1140792 5.1341E-04 5.6965E-02 109.95
2016 325 416 4527 951768 4.3689E-04 6.6983E-02 152.32
2017 294 349 5209 786059 4.4379E-04 5.3425E-02 119.38
2018 320 237 5521 546748 4.3328E-04 5.4785E-02 125.44
2019 126 70 728 75712 9.2370E-04 1.4754E-01 158.73
It can be seen from Figure 6 that for each time slice of the collaboration network,
the causal effect of citations on collaboration is strong, and the citations increase the
probability on collaboration by about 100 times. Not as obvious as the causal effect of
the same institution, but obviously stronger than the effect of keyword.
3.3 Research on performance and efficiency of network embedding based
collaboration prediction algorithms
Divide the data set into training set and test set according to the ratio of
80%-20%, 60%-40%, and 40%-60%, and discuss the performance of the seven
algorithms introduced in section 2.3. Each algorithm takes the same embedding
dimension, and the 4 numbers in each cell represent the (ROC_AUC, AUC,
F1_Score, Run time) introduced in Section 2.3.
Table. 8. Performance and efficiency of embedding algorithms
Algorithm dataset1(80% training dataset2(60% training dataset3(40% training
set,20% test set) set，40% test set) set，60% test set)
ProNE (0.8548, 0.7638, 0.7248, (0.8181, 0.6891, 0.6459, (0.8079, 0.6600, 0.6278,
16.9s) 16.13s) 16.68s)
NetMF (0.8686, 0.7554, 0.7166, (0.8370, 0.7079 , (0.8152, 0.6550, 0.6270,
19.3s) 0.6683, 21.89s) 23.27s)
Hope (0.4760, 0.3014, 0.2643, (0.5559, 0.3355, 0.2998, (0.5910, 0.3760, 0.3562,
33.7s) 26.26s) 24.31s)
LINE (0.8833, 0.7906, 0.7439, (0.8376, 0.7179, 0.6715, (0.8150, 0.6679, 0.6358,
2203s) 1788s 1297s)
Node2vec (0.8049 ,0.4355, 0.3379, ( 0.7592 , 0.3895, (0.7576, 0.3563, 0.3099,
404.6s) 0.3238, 326.5s) 244.0s)
Deepwalk (0.8062, 0.4431, 0.3379, (0.7628, 0.3960, 0.3174, (0.7528, 0.3548, 0.3083,
340.4s) 257.0s) 219.0s)
SDNE (0.5096, 0.3357, 0.2888, (0.5184, 0.3517, 0.3110, (0.5541, 0.3789, 0.3514,
1661s) 974.0s)| 609.8s)
As can be seen from Table 8 ,the larger the proportion of the training set, the
higher the effect of various algorithms. Algorithmic comparison. It seems that the
LINE model has the best accuracy, with the highest ROC_AUC, AUC, and F1_Score
values, but the running time of the LINE model is too long, which is hundreds of
times the running time of fast models such as ProNE and NetMF. The time efficiency
of classic random walk algorithms such as Deepwalk and Node2vec is moderate, but
the accuracy is slightly worse in this example, which may be related to parameter
14
selection. The accuracy of matrix factorization models such as ProNE and NetMF is
not much different from the LINE model, but the time efficiency is improved by
hundreds of times. Especially for the NetMF model, the accuracy is very small and
the time efficiency is very high. Therefore, the following experiments choose NetMF
as the network representation learning model.
3.4 Results of the multiple features fused cooperation prediction methods
based on NetMF
In this part, we select the collaboration data of a certain year as the positive
examples of the test data, randomly generate the same number of negative examples
as the test positive examples, and select the collaboration data, institutional data, and
author co-word data before that year (excluding the year). The cross-citation data is
fused as a training set. Several types of feature fusion methods discussed in Section
2.4 are used for experiments. Table 9 shows the results of single feature fusion, Table
10 shows the results of multiple features fusion, and the three numbers in the cell in
the table indicate (ROC_AUC, F1_Score, AUC), the best result in each row is
expressed in bold.
Table. 9. Single feature fusion collaboration prediction results
Year Raw Network Raw+Institution Raw+Keyword Raw+Citation
2009 (0.7053, 0.6757, (0.8551, 0.8093, (0.7047, 0.6785, (0.7287, 0.7084,
0.8176) 0.9143) 0.8157) 0.8311)
2010 (0.7579, 0.6975, (0.8603, 0.8086, (0.7278, 0.6872, (0.7794, 0.7469,
0.8279) 0.9018) 0.8085) 0.8461)
2011 (0.7644, 0.688, (0.8767, 0.7926, (0.7497, 0.6897, (0.785, 0.7099,
0.8091) 0.8922) 0.8018) 0.8216)
2012 (0.7389, 0.657, (0.854, 0.7746, (0.718, 0.6515, (0.7808, 0.7206,
0.7694) 0.869) 0.7564) 0.8089)
2013 (0.7471, 0.6674, (0.8683, 0.7813, (0.7476, 0.6834, (0.8232, 0.7528,
0.7708) 0.8744) 0.7754) 0.8341)
2014 (0.7231, 0.6385, (0.8684, 0.7719, (0.7255, 0.6531, (0.8273, 0.749,
0.7214) 0.8616) 0.7408) 0.8307)
2015 (0.7301, 0.6347, (0.8630, 0.7861, (0.7554, 0.6655, (0.8371, 0.7456,
0.7151) 0.8648) 0.7549) 0.8296)
2016 (0.749, 0.6615, (0.8977, 0.8225, (0.8024, 0.7059, (0.8688, 0.7833,
0.7011) 0.8766) 0.7742) 0.8463)
2017 (0.756, 0.6635, (0.9041, 0.8276, (0.7938, 0.6989, (0.8801, 0.7804,
0.6905) 0.8729) 0.7574) 0.8429)
2018 (0.8072, 0.698, (0.9132, 0.8268, (0.8489, 0.7463, (0.9225, 0.8282,
0.7212) 0.8743) 0.7988) 0.8807)
2019 (0.8471, 0.6196, (0.9501, 0.8275, (0.9073, 0.7373, (0.9678, 0.8118,
0.6761) 0.8343) 0.7815) 0.882)
Table. 10. Multi-feature fusion collaboration prediction results
15
Year Raw+I+K Raw+K+C Raw+I+C Raw+I+K+C
2009 (0.8374, 0.7820, (0.7301, 0.7003, (0.8550, 0.8038, (0.8547, 0.8147,
0.9064) 0.8286) 0.9154) 0.9150)
2010 (0.8627, 0.7963, (0.7547, 0.7181, (0.8652, 0.8107, (0.8826, 0.8292,
0.9012) 0.8277) 0.9041) 0.9140)
2011 (0.8542, 0.7723, (0.7936, 0.7184, (0.8833, 0.8078, (0.8842, 0.8111,
0.8782) 0.8312) 0.9009) 0.8992)
2012 (0.8496, 0.7621, (0.7697, 0.6888, (0.8792, 0.8147, (0.8804, 0.7911,
0.8637) 0.7920) 0.8924) 0.8883)
2013 (0.8726, 0.7745, (0.8198, 0.7472, (0.903, 0.8269, (0.8908, 0.8132,
0.8696) 0.8293) 0.9035) 0.8904)
2014 (0.8375, 0.7375, (0.804, 0.7000, (0.9028, 0.826, (0.8962, 0.8021,
0.8316) 0.7995) 0.8953) 0.8829)
2015 (0.8541, 0.7368, (0.8194, 0.7201, (0.9166, 0.8257, (0.8965, 0.7923,
0.8359) 0.8063) 0.9011) 0.8788)
2016 (0.8887, 0.7781, (0.8495, 0.7472, (0.9406, 0.8648, (0.9187, 0.8328,
0.8536) 0.8161) 0.9194) 0.8894)
2017 (0.8854, 0.7627, (0.8685, 0.7438, (0.9355, 0.8524, (0.9301, 0.8076,
0.8361) 0.8139) 0.9041) 0.8801)
2018 (0.9003, 0.7678, (0.9032, 0.7893, (0.9583, 0.8644, (0.9388, 0.8148,
0.8357) 0.8455) 0.9193) 0.8836)
2019 (0.9344, 0.7333, (0.9329, 0.7373, (0.9657, 0.8196, (0.9438, 0.7529,
0.7811) 0.7862) 0.8739) 0.8034)
Combining Table 9 and Table 10, it can be clearly seen that (1) Almost all
feature fusion cooperative prediction methods are larger than the original network in
ROC_AUC, F1_Score, and AUC, indicating that the feature fusion method can
improve the accuracy of cooperative prediction. (2) Same institution (I)> reference
relationship (K)> same keyword (C), which is exactly the same as the causal effect
sequence of the several types of relationships discussed in Section 3.2. (3) The
accuracy comparison of multiple features fusion methods is generally I+C> I+K+C>
I+K> K+C. In the case of simple network nodes and relationships, the I+K+C
three-feature fusion method is better than I+C two features. Multiple features can
bring more relationships and improve the prediction effect. There are relatively many
data nodes later, and the prediction result is close to the upper limit of the
predictability of the network. Adding keyword co-occurrence features may cause
more confusion in the network due to frequently occurring words, and the prediction
effect will not be further improved.
3.5 Collaboration prediction results
We randomly select scientific researchers and predict collaborators for him/her.
In this example, we selected the researchers Lin Mingyan of Albert Einstein Coll
Med, and used the NetMF of Raw+I+C feature fusion with better results in the
16
experiment in Section 3.4. Table 11 lists the top 20 authors with the highest
probability of collaboration in the future.
Table. 11. Authors with the top 20 collaboration probability
Author Institution sim Author Institution. Sim
Zheng, Albert Einstein Coll 0.9977 Delahaye, Albert Einstein 0.9783
Deyou Med Fabien Coll Med
Pedrosa, Albert Einstein Coll 0.9967 Rockowitz, Albert Einstein 0.9768
Erika Med Shira Coll Med
Chen, Jian Albert Einstein Coll 0.9965 Wijetunga, N. Albert Einstein 0.9643
Med Ari Coll Med
Zhao, Dejian Albert Einstein Coll 0.9937 Pal, Rajarshi Manipal Univ 0.9590
Med Branch Campus
Wang, Ping Albert Einstein Coll 0.9928 Carromeu, Univ Calif San 0.9240
Med Cassiano Diego
Xue, E. Albert Einstein Coll 0.9924 Marchetto, Salk Inst Biol 0.9225
Med Maria C. N. Studies
Sharma, V. Albert Einstein Coll 0.9924 Zhou, Li Albert Einstein 0.9148
P. Med Coll Med
Abrajano, Albert Einstein Coll 0.9904 Jaffe, Andrew Lieber Inst 0.8058
Joseph J. Med E. Brain Dev
Guo, Xingyi Albert Einstein Coll 0.9891 Lei, Mingxing Univ So Calif 0.7802
Med
Qureshi, Albert Einstein Coll 0.9851 Will, Britta Albert Einstein 0.7758
Irfan A. Med Coll Med
The first few authors in the results seem to be the first few authors who have
worked closely with the author, and the chances of continuing to cooperate in the
future are also very high. In the results of the collaboration recommendation, the
proportion of the same institution as the author is very large, accounting for 75%,
which is also consistent the law of scientific research collaboration.
4 Conclusion and Discussion
This paper constructs the collaboration prediction method based on
heterogeneous information fused network embedding, and conducts an empirical
analysis in the field of stem cells.
(1) Construct a author-centered heterogeneous information fusion schema, based
on information fusion theory. The predictability of the scientific research
collaboration network and the effects of institutions, co-word, and citation
information on collaboration are discussed. Experiments show that the they have an
impact on collaboration. The average causal effect analysis shows that the influence
order of the three factors is as follows(same institution> citation> keywords).
(2) The accuracy and efficiency of the network representation learning methods
for collaboration are discussed. Experiments show that the accuracy and
17
computational efficiency of the comprehensive method, and the graph representation
learning method based on the new matrix factorization (like NetMF) has achieved
good results.
(3) Construct a prediction method for scientific research collaboration based on
heterogeneous information fusion, and conduct empirical analysis in a yearly
network. Experiments show that the method of multiple features fusion can greatly
improve the accuracy of collaboration prediction. In terms of feature combination,
The combination of the same institution + citation relationship has achieved
outstanding results.
Future research will build some more complicated causal diagram under the
author-centered of information fusion framework, explore the causal effects of feature
combinations; explore more detailed methods for selecting relationships within
features, and continuously explore relationships in various information, and improve
the identification effect. Expand the framework of information fusion, and introduce
other invisible features such as research fields, research topics, and writing styles into
the framework of collaboration prediction, and continuously enrich the connotation of
the methods.
References
1. Newman M.E.J. Coauthorship Networks and Patterns of Scientific Collaboration.
Proceedings of the National Academy of the United States of America,(101) : 5200-5205
(2004)
2. Liben‐Nowell D, Kleinberg J. The link‐prediction problem for social networks. Journal of
the American society for information science and technology, 58(7): 1019-1031(2007).
3. Lü Linyuan. Link Prediction in Complex Networks. Journal of University of Electronic
Science and Technology of China, 39(05):651-661(2010).
(吕琳媛.复杂网络链路预测.电子科技大学学报,39(05):651-661.(2010))
4. Guns R, Rousseau R. Recommending research collaborations using link prediction and
random forest classifiers. Scientometrics, 101(2): 1461-1473(2014).
5. Yan E, Guns R. Predicting and recommending collaborations: An author-, institution-,
and country-level analysis. Journal of Informetrics, 8(2): 295-309(2014).
6. Shan Songyan, Wu Zhenxin. Review on the author similarity algorithm in the field of
author name disambiguation and research collaboration prediction .Journal of Northeast
Normal University(Natural Science Edition), ,51(02):71-80(2019).
(单嵩岩,吴振新.面向作者消歧和合作预测领域的作者相似度算法述评.东北师大学
报(自然科学版),51(02):71-80(2019).)
7. Zhang Jinzhu, Yu Wenqian, Liu Jingjie, Wang Yue. Predicting Research Collaborations
Based on Network Embedding.. Journal of the China Society for Scientific and Technical
Information,37(02): 132- 139 (2018).
(张金柱,于文倩,刘菁婕,王玥.基于网络表示学习的科研合作预测研究[J].情报学报,
37(02): 132 -139,(2018) ).
18
8. Yu Chuanming, Lin Aochen, Zhong Yunci, An Lu. Scientific Collaboration
Recommendation Based on Network Embedding. Journal of the China Society for
Scientific and Technical Information.38(05): 500-511(2019).
(余传明,林奥琛,钟韵辞,安璐.基于网络表示学习的科研合作推荐研究[J].情报学报
38(05): 500 - 511(2019)).
9. Wang Zhibing, Han wenmin, Sun Zhumei, Pan xuelian.Research on Scientific
Collaboration Prediction Based on the Combination of Network Topology and Node
Attributes. Information Studies: Theory & Application, (08):116-120+109(2019).
(汪志兵,韩文民,孙竹梅,潘雪莲.基于网络拓扑结构与节点属性特征融合的科研合作预
测研究.情报理论与实践,(08):116-120+109(2019)).
10. Liu P, Zheng K , Zou D. Research on Recommendation S&T colleboration based on LDA
model. Information Studies: Theory &Application.38(9): 79-85(2015).
(刘萍, 郑凯伦, 邹德安. 基于LDA模型的科研合作推荐研究.情报理论与实践, 38(9):
79-85(2015)).
11. Yu C, Gong Y,Zhao S,et al. Collaboration Recommendation of Finance Research Based
on Multi-feature Fusion. Data Analysis and Knowledge Discovery,(8): 39-47(2017).
(余传明, 龚雨田, 赵晓莉, 等. 基于多特征融合的金融领域科研合作推荐研究. 数据
分析与知识发现, (8): 39-47(2017)).
12. Lin Y,Wang K,Liu H,et al. Application of Network Representation Learning in the
Prediction of Scholar Academic collaboration.Journal of the China Society for Scientific
and Technical Information, 39(04):367-373(2020).
(林原,王凯巧,刘海峰,许侃,丁堃,孙晓玲.网络表示学习在学者科研合作预测中的应用
研究.情报学报, 39(04):367-373(2020)).
13. Zhang X,Wen Y,Xu H. A Fusion Model of Network Representation Learning and Topic
Model for Author collaboration Prediction. Data Analysis and Knowledge Discovery
(2021).
(张鑫,文奕,许海云.一种融合表示学习与主题表征的作者合作预测模型.数据分析与知
识发现: 1-19(2021)).
14. Xu H, Dong K, Wei L et al. Research on Multi-source Data Fusion Method in
Scientometrics. Journal of the China Society for Scientific and Technical
Information,37(03):318- 328(2018).
(许海云,董坤,隗玲,王超,岳增慧.科学计量中多源数据融合方法研究述评.情报学报,
37(03):318- 328(2018)).
15. Hua B,Li G. Discussion on Theory and Application of Multi-Source Information Fusion
in Big Data Environment. Library and Information Service ,59(16):5-10 (2015).
(化柏林,李广建.大数据环境下多源信息融合的理论与应用探讨.图书情报工作,
2015,59(16):5-10.)
16. Morris S A, Yen G G. Construction of bipartite and unipartite weighted networks from
collections of journal papers. Physics, (2005).
17. Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social
representations//Proceedings of the 20th ACM SIGKDD international conference on
Knowledge discovery and data mining. ACM:701-710(2014).
18. Tang J, Qu M, Wang M, et al. Line: Large-scale information network
embedding//Proceedings of the 24th international conference on world wide web.
International World Wide Web Conferences Steering Committee,1067-1077(2015).
19
19. Grover A, Leskovec J. node2vec: Scalable feature learning for networks//Proceedings of
the 22nd ACM SIGKDD international conference on Knowledge discovery and data
mining. ACM, 855-864(2016).
20. Wang D, Peng C, Zhu W. Structural Deep Network Embedding// Acm Sigkdd
International Conference on Knowledge Discovery & Data Mining. (2016).
21. Ou M, Cui P, Pei J, et al. Asymmetric transitivity preserving graph
embedding//Proceedings of the 22nd ACM SIGKDD international conference on
Knowledge discovery and data mining. ACM: 1105-1114(2016).
22. Qiu, Jiezhong , et al. "Network Embedding as Matrix Factorization: Unifying DeepWalk,
LINE, PTE, and node2vec." the Eleventh ACM International Conference ACM, (2018).
23. Qiu J, Dong Y, Ma H, et al. Netsmf: Large-scale network embedding as sparse matrix
factorization//The World Wide Web Conference. ACM: 1509-1520(2019).
24. Jie Zhang, Yuxiao Dong, Yan Wang,et al. ProNE: Fast and Scalable Network
Representation Learning.//In Proceedings of the 28th International Joint Conference on
Artificial Intelligence (IJCAI'19),(2019)
25. Lerer A, Wu L, Shen J, et al. PyTorch-BigGraph: A Large-scale Graph Embedding
System// Proceedings of the 2nd SysML Conference,(2019).
26. Fey M, Lenssen J E. Fast graph representation learning with PyTorch Geometric. arXiv
preprint arXiv:1903.02428, (2019).
27. Zhu Z, Xu S, Tang J, et al. GraphVite: A High-Performance CPU-GPU Hybrid System
for Node Embedding//The World Wide Web Conference. ACM, 2019: 2494-2504.
28. Lü L. “Link Prediction in Complex Networks”. Journal of University of Electronic
Science and Technology of China,39(05) , pp .651-661(2010).
29. Newman, M. E J. Scientific collaboration networks. I. Network construction and
fundamental results. Physical Review E , 64(1):016131(2001).
30. Newman, M. E J . Scientific collaboration networks. II. Shortest paths, weighted
networks, and centrality. Physical Review E Statal Nonlinear & Soft Matter Physics,
64(1):016132 (2001).
31. Xu X, Xu S,Zhu Y,et al,Link Predictability in Complex Networks. Complex Systems and
Complexity Science,11(01): 41-47(2014).
(许小可,许爽,朱郁筱,张千明.复杂网络中链路的可预测性.复杂系统与复杂性科学,
11(01):41-47(2014)).
32. Tan S,Qi M,Wu J et al. Link predictability of complex network from spectrum
perspective. Acta Physica Sinica,69(08):188-197(2020).
(谭索怡,祁明泽,吴俊,吕欣.复杂网络链路可预测性:基于特征谱视角.物理学
报,69(08):188-197.(2020)).