=Paper=
{{Paper
|id=Vol-2345/paper9
|storemode=property
|title=Mining Intellectual Influence Associations
|pdfUrl=https://ceur-ws.org/Vol-2345/paper9.pdf
|volume=Vol-2345
|authors=Tejas Shah,Vikram Pudi
|dblpUrl=https://dblp.org/rec/conf/ecir/ShahP19
}}
==Mining Intellectual Influence Associations==
https://ceur-ws.org/Vol-2345/paper9.pdf
BIR 2019 Workshop on Bibliometric-enhanced Information Retrieval
Mining Intellectual Influence Associations
Tejas Shah, Vikram Pudi
International Institute of Information Technology - Hyderabad, India
tejas.shah@research.iiit.ac.in, vikram@iiit.ac.in
Abstract. Within the social system of science, citation practices char-
acterize social functions like the conferral of recognition upon the work
of others as well as the acknowledgement of one’s intellectual debt. How-
ever, the structure of intellectual influence is misrepresented when only
the immediate citations and their cardinality are taken into considera-
tion. Thus, in order to better understand the associative dissemination
of influence and approximately construe the anatomy of this structure,
complex interactions in the convoluted network of authors and papers
need to be probed. Our study aims at understanding these heteroge-
neous complex interactions. For the bibliographic dataset of authors and
publications, we define proxy scores that attempt to determine the as-
sociative influence of the cited author over the citing author. In order
to harness structural connectivity of the network, we generate author
vector representations using these influence scores. Furthermore, with a
view to assess the competence of our proposed scores, we evaluate these
representations and provide an empirical study of the results obtained
with our algorithm against the baseline and also present a qualitative
analysis.
Keywords: Citation Network, Influence, Representation Learning
1 INTRODUCTION AND RELATED WORK
The contribution of an author in the form of publications holds an intrinsic
value responsible for the effective dissemination of knowledge. This knowledge
based relay that is extended by and for the scientific community results into
establishment of conceptual relationships in the form of citations. Citations and
references operate within a jointly cognitive and moral framework designed to
provide the historical lineage of knowledge and to repay the intellectual debts
through their open acknowledgement [15][7]. Thus, citation analysis has the po-
tential in providing valuable insights about the social system of science spanning
across a wide range of topics.
Effective digitization of bibliographic data has led to proliferation in the
propositions of various quantitative bibliometric performance indicators for jour-
nals, papers, authors and institutions based on citation counts and graph-based
ranking algorithms [6][17][22]. Studies dealing with influence have examined var-
ious aspects like topic level influence strength, influence propagation and its in-
direct global effect [8]; analysis of influence evolution between communities [4];
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time dependent estimation of influence for evaluating pairwise community in-
fluences [18]. Besides being extensively explored in literature, the concept of
influence has also surfaced in scholarly search engines such as Semantic Scholar.
Many of these approaches focus on influence analysis based on individual entities
and their overall impact on the network structures. However, influence relation-
ships and associations do not emerge inherently considering just the global influ-
ence. These influence associations can be understood as the degree of influence
between a pair of nodes within the network. Our work aims at studying these
pairwise influence associations eventuated between authors within the scholarly
network. The main contributions of the paper are as follows:
– We propose an algorithm that simulates the influence between the citing and
cited authors and suggest influence association scores.
– Considering issues in quantifying and tracing of influences that arise in schol-
arly communication and to harness structural connectivity of the network,
we profile authors and their interactions within the bibliographic network
using representation learning and the proposed influence scores. These rep-
resentations thus form a generic result of the proposed influence model and
their effectiveness in context of the problem statement is discussed.
– For assessment of predictive capacity of the aforementioned scores and the
thus obtained vector representations, experimental results subject to clas-
sification tasks are discussed along with comparative study against those
obtained by measures such as citation counts.
2 PROBLEM STATEMENT
2.1 Problem Formulation
Consider a publication pi written by m co-authors ai1 , ai2 , .., aim , which cites
a publication pj written by n co-authors aj1 , aj2 , .., ajn . Thus, a publication
citation network can be defined as a directed graph GP = (VP , EP ) constructed
from the list of references at the end of each publication, where VP represents
vertices (publications) and EP represents set of all directed edges between the
nodes denoting citations between publications. Consequently, an author citation
network GA = (VA , EA ) is defined by projecting this publication citation net-
work along the corresponding author(s) for each publication node in GP . For
instance, the citation link pi → pj is projected between their authors respec-
tively, thus creating m · n directed links from each of the m co-authors of the
citing publication to each of the n co-authors of the cited publication. Accord-
ingly, VA represents nodes (authors) and EA represents set of all such directed
links between the authors. Let the directed pairwise author citation link between
the citing author ai and the cited author aj be denoted as ai → aj (∀i = 1, .., m
and ∀j = 1, .., n). In the discussions that follow, we define and aim to quantify
the associative intellectual influence measure represented by I(ai , aj ) as the de-
gree to which author aj influences author ai when a citation is made from ai to
aj .
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2.2 Issues and Caveats in Quantifying Influence
Citation networks are complex networks in which causal structure exists along
the interactions between the nodes. However, consideration of just the citation
counts and primary degree of interactions (immediate neighbours) within the
citation network has its shortcomings as indicators for tracing intellectual in-
fluences [23]. Without a subjective survey of authors in conjunction with their
publications, it remains unknown as to what fraction of the work is cited that
was indeed influential directly or indirectly and whether the references exist
which had no influence of any kind yet cited due to other motivations. Such
complex interplay of multiple citer motivations have been empirically studied
and reported in previous studies as well [2][10].
Possible classes of errors in tracing scholarly influences include:
– The inclusion (or exclusion) of a reference in a bibliography does not com-
pletely indicate whether or not those references were directly or indirectly
influential for the proposition of the publication.
– Citing bias in favor of elite scientists or highly cited papers i.e. over-citation
described as the “Matthew effect” [11].
– Under-citation of fundamental scientific work is possibly noticed due to the
obliteration (of sources) by incorporation (OBI) in the established knowl-
edge [10].
– When a relevant piece of scientific work is known through an intermediate
publication, the intermediate publication serves as an intermediate influence.
However, it may remain uncited.
– Nature of citation types as they can be further categorized into organic
or perfunctory, evolutionary or juxtapositional, confirmative or negational
etc [14].
These certain and other such classes of errors exist within the citation data
due to under-inclusion and over-inclusion of references [2][10][23]. This makes
the task of tracing influences more complex.
3 ASSOCIATIVE INFLUENCE MEASURES
We propose two associative influence scores for effectively capturing intellectual
influence of the cited author over the citing author. The underlying principle
encapsulates the reasoning that the citations towards an influential publication
of the cited author and those from the finest works of the citing author are
indeed significant. Further, the net influential impact over a publication can
be fairly attributed as an aggregation of influences by all the cited publications.
Considering citing author’s temporal scholarly activity, associative influences are
instantiated for each such citing publication wherein references are made to other
publications. Thus, for a particular author pair (ai , aj ), we consider each such
instance wherein ai cites aj i.e. all such publications authored by ai wherein
a citation has been made to a publication authored by aj . This instantiated
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influence forms a component for the integral associative influence between the
author pair. Since the associative influence is a directed mapping, the influence
scores resemble the same notion of directedness. Thus, for a citation relationship
ai → aj , the proposed influence association score I(ai , aj ) represents the degree
of influence cited author aj has over the citing author ai .
3.1 Ranking Publications
In order to capture the collective nature of scholarly influence, publication-level
ranking is adopted (as opposed to researcher-level ranking). This mimics the
spread of intellectual influence among researchers via their publications. Quite
a few studies [6][21] investigate extensively this key issue of scientific credit dif-
fusion by dissecting the credit diffusion mechanism underlying both researcher
level and paper level graph-ranking methods. Their findings emphasize that sci-
entific credit is fundamentally derived from citation information between papers
rather than the derived researcher network. Our model for the influence dissem-
ination within the heterogeneous network of authors and papers thus avoids the
inaccurate allocation of scientific credit among researchers that potentially arises
in graph-ranking methods.
PageRank [1] takes into account the number and quality of links while mea-
suring the importance of entities within a network. Using PageRank over GP ,
the importance of publications is thus measured considering the number of ci-
tations and reputation of the papers [9]. For the publication level ranking, we
have:
(1 − α) X P R(pj )
P R(pi ) = +α· (1)
N T (p )
j→i out j
where j → i implies paper pj referring paper pi , P R(pj ) denotes the PageRank
of paper pj and Tout (pj ) denotes the number of outbound links from paper pj .
With certain empirical studies, Chen et al. [3] showed that, scientific papers
usually follow a shorter path of about average two links. This is in opposition
to six hyperlinks for the web considering the individual surfer illustration as
mentioned in the original study [1]. Accordingly, we set the damping factor α to
0.5 for the purpose of our studies as well.
3.2 Source based Influence - IS (ai , aj )
The basis for this score is that, citations from the significant works of the cit-
ing author (ai ) are indeed relevant, considering the intellectual and cognitive
influences. The citing author’s significant works can be regarded as those schol-
arly works which have a relatively high PageRank among other works of the
same author. The instantiated associative influence components resulting from
citing author’s citation activity sum up to form the net score. Thus, we calculate
IS (ai , aj ) as follows:
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P P R(pik )
k Tout (pik ) ∀pik ∈ pik → paj (2)
IS (ai , aj ) =
P R(pai )
where, pik denotes the k th publication of the citing author ai wherein a cita-
tion has been made to a publication authored by the cited author aj , P R(pik )
represents the PageRank of the citing publication pik , Tout (pik ) denotes the
number of outbound links (references) from publication pik and P R(pai ) is the
normalization factor which is the average PageRank value of all the publications
authored by ai . The normalization factor accounts for the variance in the ranks
of citing publications. So, higher the normalized weight of each such component
(i.e. higher the normalized PageRank of citing publication) and higher the cardi-
nality of such components exchanged between ai and aj , higher is the associative
influence.
3.3 Destination based Influence - ID (ai , aj )
The underlying notion for devising this score is to calculate a measure of the
extent to which the citing author tends to cite a set of authors weighted by
their influential scholarly contributions in his/her publications. Extending the
influence instantiation notion for ID , the association components of influence
comprise of the PageRank of the cited publication and the inbound links of
that cited publication (distributing impact of a publication among the inbound
citation references). Thus, we calculate ID (ai , aj ) as follows:
P P R(pjk )
k Tin (pjk ) ∀pjk ∈ pai → pjk (3)
ID (ai , aj ) =
P R(paj )
Here, pjk denotes the k th publication of the cited author aj wherein a citation
has been made from a publication authored by the citing author ai , P R(pjk )
represents the PageRank of the cited publication pjk , Tin (pjk ) denotes the num-
ber of inbound links to publication pjk and P R(paj ) is the normalization factor
which is the average PageRank value of the publications authored by aj . The
disparity in the ranks of cited publications is accounted for by this normalization
factor. So, more the normalized weight of each such association components and
higher the cardinality of such components exchanged between ai and aj , higher
is the associative influence.
3.4 Qualitative Analysis
Author and publication ranks of prominent scientists and young researchers span
across a wide spectrum. For example, a highly cited publication of a prominent
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scientist might be influential but in a modest way to each individual researcher
in the scientific community at large. Also, the finest works of a young researcher
possibly might have less inbound citations comparatively, however, the substan-
tial influence of cited publications within such publications should not possibly
be overlooked. Considering the different degrees of author and publication ranks,
it can be seen from Equation (2) that significant works of young as well as even
mediocre researchers are instrumental in contributing towards highlighting the
cited author’s influence. Thus, irrespective of the cited author’s prominence,
his/her influence over the citing author is conspicuous and visibly pronounced.
Also, from Equation (3) it is evident that even scientists not belonging to the
higher order of ranks receive their due scholarly attribution pertinent to the their
respective notable scientific studies. Such notions of relative author impact and
allocation of due credit persist across the spectrum for most of the researchers
belonging to different degrees of author ranks. Normalization of associative in-
fluence measures as illustrated in sections 3.2 and 3.3 account for such variances
along with taking into consideration the effect of accumulated advantage (i.e.
Matthew effect [11]).
Associative pairwise influences eventuated due to scholarly contributions over
time semantically imply that there is certain form of influence of the cited au-
thor over the citing author irrespective of the citation types. In this paper, we
focus on the existence and extent of conceptual relationships formed between au-
thors. However, the precise nature of influence maybe hard to quantify without
the factual ontological citation representations. For alleviating issues concerning
under-inclusion of references (as discussed in Section 2.2), capturing the net-
work neighborhood and harnessing the structural connectivity, we profile authors
and their interactions using representation learning and the proposed influence
scores. This effectively maps the latent features within the citation network into
a vector space.
4 AUTHOR REPRESENTATION LEARNING
Recent work in language modeling and representation learning such as Word2Vec [12]
focuses on application of probabilistic neural networks which map words into vec-
tor spaces. The author vector representations are learned with a similar intuition
as discussed in the following sections:
4.1 Random Walk on Weighted Directed Network
Using proposed influence measures, we model weighted random walks over the
author citation network. These walks can be approximated as sentences in the
context of language modeling. Analogous to recent researches [16], it represents
a network as a “document”. The motivation behind converting a graph into a
series of text documents is: Word frequency in a document corpus and the visited
node frequency during a random walk for a connected graph, both follow the
power law distribution [16].
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We sample random walks over the weighted directed author citation network.
Considering the author citation network (GA ) with n author nodes (a1 , a2 , . . . ,
an ); wij represents the weight of the edge connecting nodes ai and aj where
the edge weight is the influence score I(ai , aj ) (as derived from Equation 2 or
Equation 3) for the author pair and therefore we have, wij = I(ai , aj ). Since
the influence scores are non-negative, we have wij ≥ 0. For the study of tracing
influence associations, we prune self-citation loops and thus wii = 0. Now, for
a given source node ai , the transition probability that the author node aj is
chosen from the direct successors of ai is proportional to the influence measure
I(ai , aj ). This is computed as:
I(ai , aj )
pai ,aj = P (4)
I(ai , ak )
k
where pai ,aj represents the transition probability. For each source author
node ai , we simulate a weighted directed random walk Wai . This sampling is
a stochastic process consisting of author nodes wa1i , wa2i , . . . , wani as random
variables such that waj i is a vertex chosen with transitive probability pai ,aj from
the direct successors of ai . In our experiments we set the length of these walks
to be fixed. For each source vertex, the random walk generator samples author
nodes based on respective transition probabilities until a maximum length l (=
40) is reached. For the purpose of our study, we generate such weighted random
walks γ (= 15) times for each author.
4.2 Representation Learning Framework
Modelling social structures and relationships within networks can be aligned
with the optimization techniques used to model natural languages [16][5]. With
the ordered sequence of nodes constructed using weighted random walks, we
learn representations using Skip-gram model. This represents authors and the
citation relationship shared between a pair of authors in an unsupervised manner.
Based on distributional hypothesis, these representations are latent features that
capture neighbourhood as well as structural influences in the citation network
in a continuous low dimensional vector space. In effect, these representations
encapsulate more information and relationships between authors than using just
the immediate citations.
Skip-gram is a language model that maximizes the conditional co-occurence
probability of words occurring within a predefined window [12]. Thereby, we
have f : VA → Rd as the mapping function from nodes to feature representations
that we aim to learn. Here d specifies the number of dimensions of the feature
representation and f represents a matrix of size |VA | × d parameters. Now, we
try to optimize the likelihood function as formulated in Equation 5:
j=i+w
X
max log Pr(aj | f (ai )) (5)
f
j=i−w,j6=i
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where w is the size of the window, ai ∈ VA and Pr(aj | f (ai ) is defined by
the softmax function:
exp(f (aj ) · f (ai ))
Pr(aj | f (ai )) = P (6)
exp(f (ak ) · f (ai ))
k∈V
Skip-gram assumes that inside the context, all nodes are independent of each
other and are equally important. However, as seen from Equations 5 and 6,
update step per node is proportional to |VA |. This is computationally expensive
for large networks (such as the author citation network in consideration). We
approximate the optimization function using negative sampling [13].
Using the obtained resultant author vector representations, we validate the
effectiveness of our proposed scores as discussed in the following sections.
5 PERFORMANCE MODEL
Limitations in the assessment of intellectual and cognitive influences prevail due
to its subjective nature. However, despite the shortcomings of citation data,
studies [23] assert that citations can be used as approximate proxy indicators of
influence for the aggregates of authors and papers. Collectively, with measurable
factors and practical limitations of the study, it can be fairly argued that if a
publication proves to be relatively influential in the scientific work of an author,
then he/she is quite definitive to have a higher relative citation ratio towards the
cited influential authors. So, we utilize and bucket these relative citation ratios
for author pairs as labels for classifying the extent of influence. Representations
obtained using influence scores can be evaluated against baseline by a compar-
ative study of citation prediction and its extent between author pairs. In our
study, the purpose is to evaluate whether our proposed influence measures cap-
ture meaningful relationships. To do so, it suffices to test their relative capacity
in citation prediction, and the absolute predictive accuracy is not the criterion
being assessed.
5.1 Validating Influence Associations
To capture the semantic relatedness between the citing and cited author’s in-
fluential relationship and in order predict weighted citation link between a pair
of authors a1 and a2 , we generate edge representation e(a1 , a2 ). This is done
by defining a binary operator over the corresponding author feature vectors
f (a1 ) and f (a2 ). Similar strategies have been successfully used in earlier studies
for link prediction tasks [5]. We define ith component of the edge representa-
tion ei (a1 , a2 ) as concatenation of author feature vector components denoted by
fi (a1 ) t fi (a2 ). e(a1 , a2 ) spans across R2×D as the author feature vectors ∈ RD
are concatenated.
These edge representations are now further used in training and evaluation
for predicting the influence between a pair of authors. However, predicting the
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presence of link between the author pair in the testset is necessary but insuf-
ficient to assess predictive capacity of the degree of influence. For evaluating
the extent of influence more rigorously, we extend this binary classification link
prediction evaluation to multi-label classification. Here, the edge representations
are classified against labels viz., Nil Influence (NI), Slightly Influenced (SI) and
Highly Influenced (HI) depending upon the relative citation ratio between the
pairs of authors in the training and testing sets respectively. Thus, for a pair
of authors ai and aj , e(ai , aj ) is classified based on the relative citation ratio
(cr(ai ,aj ) ) which is computed as:
c(ai , aj )
cr(ai ,aj ) = P (7)
c(ai , ak )
k
where c(ai , aj ) represents number of citations from the citing author ai to
the cited author aj in the author citation network during that specific temporal
segment. The calculated citation ratios are then mapped to aforementioned class
labels. If cr(ai ,aj ) ∈ (0.0, δ], then e(ai , aj ) is classified as SI. When cr(ai ,aj ) > δ,
then e(ai , aj ) is classified as HI. Based on repeated experiments to maximize
discrimination among citation ratios, δ is set to 0.036. Lastly, cr(ai ,aj ) = 0 implies
that there is no influence of the cited author aj over the citing author ai , thus,
classifying e(ai , aj ) as N I.
Baseline: For comparing the performance of our model and the influence
scores, we use citation counts as baseline for our evaluation. Author profiling
and generation of vectors for this baseline is achieved using weighted random
walk over author citation graph. Here, weights are the number of citations from
the citing author to the cited author (as opposed to influence measures as dis-
cussed in 4.1). Edge representations for this baseline are then computed using
the obtained author vector representations.
6 EXPERIMENTAL DESIGN
6.1 Dataset Description
The DBLP dataset used consists of papers published from the period 1960 to
2014 wherein the citation data is enriched by using bibliographic metadata from
ArnetMiner [20]. The dataset contains information such as paper’s title, its
authors and their affiliations, citation list, publication year, etc. For our ex-
periments, pre-processing is performed over the dataset for pruning incomplete
records where: 1) The publications with incomplete meta data (absence of year,
authors, etc.) are removed. 2) Internal citations can be defined as references to
publications within the snapshot of dataset being considered. References other
than these internal citations are removed.
The final dataset includes 1277594 papers and 1003387 authors. The total
count of internal references for publication citation network is 7962820 whereas
edgelist for the author citation network sums up to 39713499.
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105 105 105
105
104 104 104
104
# Publications
# Publications
# Authors
# Authors
103 103 103 103
102 102 102 102
101 101 101 101
100 100 100 100
100 101 102 103 104 100 101 102 103 100 101 102 103 104 100 101 102 103
# Citations # References # Citations # References
(a) (b) (c) (d)
Fig. 1: Figures (1a), (1b) represent publication in-degree and out-degree distributions
for the publication citation network and figures (1c), (1d) denote the author in-degree
and out-degree distributions for the author citation network respectively.
6.2 Dataset Segmentation
In order to evaluate and assess the degree of influence as quantified by the
suggested influence measures, we consider work of individual authors over a
temporal scale. We divide the dataset into 3 segments as follows:
Profiling: This segment of dataset represents the activity of researchers
and scientists within the bibliographic network up to 2006. Interactions between
researchers by means of collaborations, citations and conceptual exchanges in
the form of publications are significantly eventuated considering such a wide
temporal span. This partition of dataset helps in the process of author profiling
by means of learning author vector representations using weighted random walks.
Training: This segment of dataset is used for learning influence associa-
tions by generating edge representations using the author vector representations
captured in the Profiling segment. The exact testset author pair is excluded
from this segment to avoid over-fitting of the classifier. Author pairs for whom
citations have been eventuated between 2006 and 2010 are considered for this
segment.
Testing: The proposed influence scores and the baseline (citation count) are
validated using the bibliographic interactions in this segment. An author pair is
valid for testing as long as we have individual vector representations for both the
authors. Citation exchanges resulted since 2011 are considered for this segment.
6.3 Evaluation and Results
Using the Training set, edge representations are constructed for each pair of au-
thors between whom citations are exchanged during this segment. Consequently,
relative citation ratios between these author pairs are calculated using Equation
(7). The edge representations are then classified with the class labels namely,
(N I, SI and HI) using RandomForest. For authors spanning across wide spec-
trum of ranks and extent of influences, this enables us to capture what kind of
authors cite what kind of authors. Since we aim at comparing the relative pre-
dictive capacity of these representations, we focus less on exact classifier settings
and report results achieved by each representation using the same parameters.
On evaluation, precision, recall and f-scores for baseline and influences mea-
sures are as shown in the tables (1), (2) and (3). From these results, we can see
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that even the resultant representations obtained using baseline citation count
performs well enough for the multi-label citation prediction task. However, in-
fluence measures IS and ID can be clearly seen as better performers almost
throughout for each of the aforementioned classes, considering the reported
precision-recall values. We observe that, for class N I, almost all the three mea-
sures perform equally. This can be attributed to the better accuracy of classifier
for non-existent edges between author pairs, irrespective of the weights in consid-
eration. It can also be theorized that certain influence associations may traverse
from a class to another over a span of time. For example, the citations of the cit-
ing author with higher influence of the cited author possibly might get narrowed
down (and vice versa) over a period of time. This can happen due to various pos-
sible reasons such as a shift in research trends, cultivation of interests in newer
fields, etc. Thereby, we also witness recall values for class HI on lower sides for
all the three measures.
Table 1: Citation Count Table 2: Influence (IS ) Table 3: Influence (ID )
Label Precision Recall F-Score Label Precision Recall F-Score Label Precision Recall F-Score
NI 0.93 0.89 0.91 NI 0.93 0.90 0.91 NI 0.94 0.89 0.91
SI 0.72 0.81 0.76 SI 0.75 0.83 0.79 SI 0.75 0.85 0.80
HI 0.71 0.49 0.58 HI 0.74 0.54 0.62 HI 0.76 0.55 0.64
7 CONCLUSION AND FUTURE WORK
In this paper, we present a model to trace intellectual influences harnessing the
structural connectivity within an academic network. Further, we generate author
profiling by mapping the latent features into a vector space using the proposed
influence scores. We also evaluate effectiveness of the captured author relation-
ships and the resultant author representations by performing experiments for
classification tasks, such as citation prediction and the extent of citation. It is
observed that results obtained using the suggested influence scores perform bet-
ter as compared to immediate citation counts. A future direction would be to
incorporate types of citations (as mentioned in Section (2.2)) into the current
model, possibly using ontological representation of citations [19]. This might help
us in knowing and gaining insights on the nature of influences. It would also be
interesting to analyze effects of research trends on influences associations.
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