Exploiting Social Networks for Publication Venue Recommendations

Hiep Luong

, Tin Huynh

Susan Gauch

and Kiem Hoang

University of Arkansas, Fayetteville, AR 72701, U.S.A.

University of Information Technology, Ho Chi Minh City, Vietnam

Keywords: Recommender Systems, Social Network Analysis, Publication History, kNN, Machine Learning.

Abstract: The impact of a publication venue is a major consideration for researchers and scholars when they are

deciding where to publish their research results. By selecting the right conference or journal to which to

submit a new paper minimizes the risk of wasting the long review time for a paper that is ultimately

rejected. This task also helps to recommend appropriate conference venues of which authors may not be

aware or to which colleagues often submit their papers. Traditional ways of scientific publication

recommendation using content-based analysis have shown drawbacks due to mismatches caused by

ambiguity in text comparisons and there is also much more to selecting an appropriate venue than just

topical-matching. In our work, we are taking advantage of actual and interactive relationships within the

academic community, as indicated by co-authorship, paper review or event co-organizing activities, to

support the venue recommendation process. Specifically, we present a new social network-based approach

that automatically finds appropriate publication venues for authors’ research paper by exploring their

network of related co-authors and other researchers in the same field. We also recommend appropriate

publication venues to a specific user based on her relation with the program committee research activities

and with others in her network who have similar paper submission preferences. This paper also presents

more accurate and promising results of our social network-based in comparison with the baseline content-

based approach. Our experiment, which was empirically tested over a large set of scientific papers published

in 16 different ACM conferences, showed that analysing an academic social network would be useful for a

variety of recommendation tasks including trend of publications, expert findings, and research

collaborations, etc.

1 INTRODUCTION

The World Wide Web and its evolving infrastructure

have played a significant role in the information

explosion. This voluminous amount of unstructured

and semi-structured data creates “big data,” data sets

that grow so large that they become awkward to

work with or analyse using existing data

management approaches. With the fast growth of

digitalized textual data and documents, there is an

urgent need for powerful text information

management tools to help users find exactly what

they are looking for and to help researchers keep

abreast of information of whose existence they may

be unaware. Recommender systems are one

approach to helping users deal with the flood of

information. They are tools that automatically filter

a large set of items, e.g., movies, books, scientific

papers, music, etc., in order to identify those that are

most relevant to a user’s interest.

There are a wide variety of dissemination outlets

for research results, e.g., conferences, journals,

seminars, scientific forums. When an author has a

paper that they want to share, the review cycle can

be time consuming and, if the paper is rejected

because it is not a good fit, valuable time can be lost.

In computer science, in particular, the pace of

innovation is high. Selecting the right publication

venue the first time is particularly important.

Traditional techniques usually use citation-based

metrics with certain bibliometrics, such as the

Impact Factor (Garfield, 1955), to measure the

reputation and quality of publication. However,

these techniques require frequent updates to the

bibliometrics in order to maintain an accurate impact

factor.

Recently there has been considerable interest in

applying social network-based methods for ranking

conference quality (Yan and Lee, 2007), seeking

239

Luong H., Huynh T., Gauch S. and Hoang K..

Exploiting Social Networks for Publication Venue Recommendations.

DOI: 10.5220/0004140102390245

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2012), pages 239-245

ISBN: 978-989-8565-29-7

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

research collaborators (Chen et al., 2011) or

generating recommendations (Klamma et al., 2009);

(Luong et al., 2012). Recommendation systems are

particularly important for researchers and scholars in

their professional research activities. For some

experts in a research domain, or senior researchers

who have strong publication records, selecting a

conference might be a trivial task since they know

well which conferences, journals, or scientific

forums are the best places in which to publish their

research papers. However, new researchers with less

experience may not be able to easily assess

conferences and the may not be current on relevant,

new publication venues. The widespread use of the

Internet allows researchers to create large,

distributed academic social networks that can be

analyzed to further enhance research productivity.

Our interest is in how to use these academic social

networks to recommend appropriate publication

venues to authors for an unpublished paper.

In this paper, we present a survey of current

research on content-based and collaborative filtering

recommender systems, and recent trends applying

social network analysis in recommender systems in

section 2. We will focus mainly on recommendation

research for academic activities and digital libraries.

In section 3, we present our social network-based

publication venues recommendation. Section 4

presents our content-based recommendation

approach. Next, we present and discuss some

experimental results for both appoaches in section 5.

The final sections present our conclusions and

discuss our future work in this area.

2 RELATED WORK

Traditional recommender systems are usually

classified as content-based, collaborative, or hypbrid

based on the type of information that they use and

on how they use that information (Adomavicius and

Tuzhilin, 2005). Content-based approaches compare

the contents of the item to the contents of items in

which the user has previously shown interest.

Automated text categorization is considered the core

of content-based recommendation systems. (Yang et

al., 1999) reported a controlled study with statistical

significance tests on five text categorization

methods: Support Vector Machines (SVM), k-

Nearest Neighbors (kNN) classifier, neural network

approach, Linear Least-squares Fit mapping and a

Naïve Bayes classifier. Their experiments with the

Reuters data set showed that SVM and kNN

significantly outperform the other classifiers, while

Naïve Bayes underperforms all the other classifiers.

In other work, kNN was found to be an effective and

easy to implement that could, with appropriate

feature selection and weighting, outperform SVM

(Cunningham and Delany, 2007).

Collaborative Filtering (CF) determines

similarity based on collective user-item interactions,

rather than on any explicit content of the items. (Su

and Khoshgoftaar, 2009) has summarized a detail

review of some main CF recommendation

techniques. In another recommendation research

using CF.

The online world has supported the creation of

many research-focused digital libraries such as the

Web of Science, ACM Portal, Springer Link, IEEE

Xplore, Google Scholar, and CiteSeerX. Recently,

new research is focusing on these as enablers of a

community of scholars, building and analyzing

social networks of researchers to extract useful

information about research domains, user

behaviours, and the relationships between individual

researchers and the community as a whole.

Microsoft Academic Search (MAS), ArNetMiner

(Tang et al., 2008), and AcaSoNet (Abbasi and

Altmann, 2011) are online, web-based systems

whose goal is to identify and support communities

of scholars via their publications. The entire field of

social network systems for the academic community

is growing quickly, as evidenced by the number of

other approaches being investigated (Abbasi and

Altmann, 2011); (Miki et al., 2005) and (Mika,

2005).

In order to extracting useful information from an

academic social network, (Zhuang et al., 2007)

proposed a set of novel heuristics to automatically

discover prestigious (and low quality) conferences

by mining the characteristics of Program Committee

members. (Chen et al., 2011) introduces CollabSeer,

a system that considers both the structure of a co-

author network and an author’s research interests for

collaborator recommendation. CollabSeer suggests a

different list of collaborators to different users by

considering their position in the co-authoring

network structure. In work related to publication

venues recommendation, (Pham et al., 2011)

proposed a clustering approach based on the social

information of users to derive the recommendations.

To our best knowledge, we have not seen

existing research that exploits the relationships

between the authors of an unpublished paper with

conference PC members or people in the social

network who have previous publications to

recommend appropriate publication venues.

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3 SOCIAL NETWORK-BASED

RECOMMENDATION

APPROACH

3.1 Overview

We introduce a new approach to recommending a

list of appropriate conference venues to an author for

their unpublished paper by using a large-scale

network of researchers. By analysing this large-scale

social network, we recommend publication venues

to the unpublished paper’s authors based on the

‘similarity’ they have with conference PC members

or with other authors with papers published in the

conferences.

To build our dataset, we selected four

subdomains of research in Computer Science

corresponding to four SIGs (Special Interest

Groups). We have chosen four different fields to

challenge our recommendation task. Then, we

manually picked four different conferences for each

SIG. The total list of 16 selected conferences was

presented in our previous work (Luong et al., 2012).

For each of these 16 conferences, we downloaded all

published papers from 2008-2010 from the ACM

digital library as well as the list of Program

Committee (PC) members for each conference. We

have built a truth-list of correct paper-conference

relation to evaluate our conference recommendation

approaches.

3.2 Academic Social Network Analysis

We measure the closeness between authors and

conferences by two new methods, the first based on

the relationships between the candidate paper

authors and the PC members, the second between

the candidate paper authors and previously authors

previously published in each conference.

This problem can be formalised as a relatedness

calculation of vertices in a graph. Each researcher

can be viewed as a vertex of the graph, and the

graph edges represent co-author relationships with

other researchers in the network. Note that a member

(a vertex) in the network can be either a paper

author, a PC member, or both. Figure 1 represents

part of an academic social network in which A

and

are seeking an appropriate publication venue for

their jointly authored, unpublished paper. In this

figure, the blue nodes represent the candidate paper

authors, the white nodes other researchers, and the

orange nodes represent a particular conference’s PC

member. In our initial work, we weight all edges

with 1 for simplicity.

Figure 1: Example of a part of the social network.

The closeness between authors is calculated

based on the shortest path connecting them. For

example, to determine the relation similarity

between author A

and a PC member F, there are

several paths between these two nodes such as A

D-F, A

-B-D-F, A

-B-C-E-F, etc. However, their

shortest path, A

-D-F, contains 2 edges. The shorter

the path, the closer the nodes are. Thus, closeness

between two nodes in a graph, A and B, is the

inverse of path length calculated as:

(, )

Closeness A B

ShortestPath A B



3.2.1 Closeness between Paper Authors and

PC Members (Author_PC Method)

In this method, we recommend that a paper be

submitted to the conference with the strongest

relationship between the paper’s authors and the

conference’s program committee. Essentially, this

measures the impact that being an insider has to the

likelihood of a paper being accepted. The more close

relationships that a paper’s authors have with PC

members, the more likely that conference is to be

recommended for the paper. Note that this favors

conferences with large program committees and, in

future work, we will investigate the effects of

normalizing these weights by the number of PC

members.

In particular, for conference i, we add together

the strength of the relationship between each of the

paper’s co-authors with the PC as follows:

_(,)

Author PC Closeness m i





where A is the number of authors of the unpublished

paper and Closeness(m,i) is the closeness between

an author m and the program committee for

ExploitingSocialNetworksforPublicationVenueRecommendations

241

conference i. Specifically, Closeness(m, i) is the sum

of the closeness between that author and any

member of the PC.

(,) (, )

Closeness m i Closeness m PC





with m is an author of the paper, PC

i,j

is the j

program committee member for conference i, and N

is the total number of PC members for that

conference. The conference with the highest value is

recommended as the publication venue.

3.2.2 Closeness between Paper Authors and

Previous Conference Authors

(Author_NetAuthors Method)

In this method, we consider how close a paper’s

authors are to those in the network who have

published their paper(s) in a specific conference.

Essentially, this is based on the belief that, if papers

authors academic colleagues have had their work

published by a particular conference in the past, this

is a good indication that this paper is also likely to

be acceptable. This relation similarity can be defined

as following:

(, )

uthor NetAuthors Closeness m NetAuthors





where A is a number of author(s) of the unpublished

paper, and Closeness(m, NetAuthors

) is the

closeness between an author m and all other authors

in the network who have published their papers to

the conference i. This value Closeness(m,

NetAuthors

) is also calculated as the sum of the

closeness between that author with any other author

relevant to the conference i.

(, ) (, )

iij

Closeness m NetAuthors Closeness m Author





with m is an author of the paper, Author

i,j

is the j

author in the network who has paper published in the

conference i. M is total number of all members that

have papers published in the conference i. We

recommend the conference with the highest value as

a publication venue for the unpublished paper.

4 EXPERIMENTS

4.1 Dataset

In order to get the publication history of authors, we

developed a focused crawler in Java that extracts all

co-authors and relevant publications for a given

author from the Microsoft Academic Search (MAS)

website. As presented in the section 3.1, our input

contains 16 ACM conferences of 4 SIGs. With all

papers collected from 2008-2010, we used the

papers published in two years 2008 and 2009 as

training documents and the ones published in 2010

as test documents for the classification task. Since

the number of papers published for each conference

varies, we randomly selected 20 documents per year

per conference (60 totals). Thus, each conference

had 40 training and 20 test documents. With 16

conferences, the total test collection contained 640

training and 320 test documents. We split the 320

test documents into two sets: 160 for tuning and 160

for validation. For each of the 16 conference

instances, we also downloaded the names of the

program committee members from the conference

website for the year 2010.

For each paper in the test collection, we

extracted the author names and used a crawler to

gather information about each author’s publications

and co-authors. The co-authors of the co-authors

were then recursively collected, until a network 3

levels deep was created. As a result, we collected

information about 306,227 authors and 392,878

papers. We also submitted the names of the PC

members to MAS to collect their authorship

relationship. This information was downloaded and

stored in a database. We manually reviewed authors

with large numbers of publications to remove

publications that were incorrectly attributed to an

author due to the ambiguity of the author’s name.

Finally, we built a large-scale graph, representing

the network of researchers and experts, containing

303,843 vertices (authors) and 1,220,472 edges (co-

authorship relationships with an average node

degree of 8.03.

4.2 Baseline

In order to evaluate our new recommendation

approach using academic social networks, we

compared the methods described in section 3 with a

baseline of content-based recommendations. In the

baseline, conferences in the dataset are treated as

different categories into which we classify a

candidate paper. Each paper is represented by a

vector of terms weighted by TF-IDF. The similarity

between two papers is calculated using the cosine

similarity measure. Papers are classified into

conferences using a k-Nearest Neighborhood (kNN)

algorithm (Gauch et al., 2004) trained using the 640

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training documents. In order to identify the best k

value for our experiment, we varied k, the number of

classifier results used for to select the conference,

from 3-40. Based in previous testing (Luong et al.,

2012), we report our best performing k (i.e., k = 25)

at which the content-based approach reached the

highest classification precision. We also compare the

performance of our new research to our previous

work, i.e., PubHistory that recommends conferences

to authors based on their own publication histories,

rather than the publication histories of researchers in

their academic social network (Luong et al., 2012).

4.3 Evaluation Metrics

We evaluated each method’s performance using

precision, i.e., the percentage of the time that the

recommender system recommends the conference

and/or SIG in which the test paper actually

appeared:

 Conference Precision: measures the percentage

of time that the recommender recommends the true

conference. This is reported at various cut-offs, i.e.,

Top1 means that the correct conference was the top-

ranked recommendation etc.

 SIG Precision: measures the percentage of time

that the recommender recommends a conference

from the correct SIG. Since there are fewer SIGS,

and they differ more than the conferences do, this is

an easier task.

4.4 Results and Discussion

Table 1 summarizes the conference precision results

for each of the four methods using the 160 tuning

documents. The conference precision results are also

shown graphically in Figure 2.

Table 1: Conference precision results using four different

methods.

Conference Precision

Top1 Top2 Top3 Top4

Content-

Based

48.8% 68.8% 80.6% 90.6%

PubHistory 66.2% 83.8% 95.5% 96.8%

Author_PC 43.6% 59.6% 69.9% 77.6%

Author_Net

Authors

74.5% 91.0% 94.3% 96.8%

These results show that the Author_NetAuthors

method is by far the most accurate method for

recommending a conference. It recommends the

correct conference as the top choice 74.5% of the

time and the correct conference is within the top 4

choices almost 97% of the time. Authors tend to

submit to, and be accepted by, conferences in which

their co-authors (direct or indirect) have previously

been published. Interestingly, this method

outperforms the PubHistory method (66.2% Top1

conference precision) that recommends conferences

based only on the author’s own publication history.

The difference is most evident in the Top1 and Top2

conference precision. Clearly, information from the

author’s academic social network helps identify

good publication venues.

Figure 2: Comparison of classification precisions.

The two methods based on publication history,

Author_NetAuthors and PubHistory, both

outperform the _content-based baseline method by a

wide margin. The content-based baseline achieved a

Top1 conference precision of only 49%. The

Author_PC method (43.6% Top1 conference

precision) is the only method that underperforms the

Content-Based method. This is actually a very

positive result for the integrity of the research

community. It demonstrates that relationships

between authors and PC members does not predict

acceptance of a paper at a conference. It is more

important that the paper be of interest to the

community, and on topic, than that the author has

worked with someone on the PC in the past.

Table 2: SIG precision using four different methods.

Methods SIG Precision for the top-ranked

Content-Based 80.63%

PubHistory 87.66%

Author_PC 74.36%

Author_NetAuthors 93.00%

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Since there are 16 conferences that overlap in

topics, but only 4 SIGs that cover different research

areas, predicting the correct SIG should be an easier

task than predicting the correct conference. Table 2,

which summarizes the SIG precision results for the

top-ranked result of four methods, confirms this

hypothesis. These results confirm our hypothesis

that a publication venue recommendation system can

benefit from social network analysis instead of, or in

addition to, traditional content-based approaches.

6 CONCLUSIONS

The goal of this research is to implement and

evaluate a new approach to recommend publication

venues for an unpublished article. Our approach

takes advantage of information analysed from an

academic social network of researchers linked by

their co-authorship relationships. The results show

that the Author_NetAuthors approach that

incorporates relationships between a paper’s

authors’ academic social network and each

conference’s network of previously published

authors is the best performing result. Overall, we

conclude that social network-based approaches can

outperform content-based approaches when

recommending publication venues. They work well

even when deciding between conferences that

overlap in topics, a task that is very difficult for

content-based recommender systems. We also

showed that relationships with the community of

authors who publish in specific conferences is more

important than relationships with members of the

conference’s program committee members.

Our main tasks in the future are to enhance the

publication venue recommendation system by

developing algorithms that take into account more

sophisticated graph relationships and different kinds

of links in the network such as citation and other

indications of research collaboration (e.g.,

researchers from the same institution).

ACKNOWLEDGEMENTS

This research is partially supported by the NSF grant

number 0958123 - Collaborative Research: CI-

ADDO-EN: Semantic CiteSeer

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