On social networks of program committees: Structure and effect on paper acceptance fairness

Chen Avin, Zvi Lotker, David Peleg, Itzik Turke

Research output: Contribution to journalArticlepeer-review


How do the social graphs of Technical Program Committees of conferences look like? and how can studying these social graphs help us to decide whether there is a bias in paper selection processes for conferences? This work empirically studies the structure of program committees’ social graphs, their unique structure and characteristics, and examines the existence of a bias in favor of the collaborators of the program committee members. Specifically, it checks whether a paper written by a past collaborator of a program committee member is more likely to be accepted to the conference (which may be interpreted as indicating a bias on behalf of the program committee members). Twelve ACM/IEEE conferences over several years were studied, and the social network of each annual meeting of a conference was constructed. The vertex set of such a network consists of the program committee members and the authors of the papers accepted to the meeting, and two researchers are neighbors in the network if they were collaborators (namely, have co-authored a paper) prior to the meeting. For each meeting network, the coverage of the program committee in the network, namely, the ratio between the number of authors that are collaborators of the program committee and the total number of the authors-vertices of the meeting, was calculated. The coverage of the real meeting’s social networks was compared to the coverage in artificial meetings. A program committee is viewed as coverage biased if its coverage is significantly higher than that of corresponding artificial meetings of the conference. Our findings show that, although there are some coverage biased program committees, for most conferences the coverage in the real meetings is the same as, and sometimes lower than, that of the artificial ones, indicating that on average there is probably no bias in favor of papers written by collaborators of the program committee members for these high-quality conferences.

Original languageEnglish
Article number18
JournalSocial Network Analysis and Mining
Issue number1
StatePublished - 1 Dec 2016


  • Bias
  • Centrality
  • Conference
  • Fairness
  • Program committee
  • Star graph

ASJC Scopus subject areas

  • Information Systems
  • Communication
  • Media Technology
  • Human-Computer Interaction
  • Computer Science Applications


Dive into the research topics of 'On social networks of program committees: Structure and effect on paper acceptance fairness'. Together they form a unique fingerprint.

Cite this