Weighted simplicial complex

A novel approach for predicting small group evolution

Ankit Sharma, Terrence J. Moore, Ananthram Swami, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study of small collaborations or teams is an important endeavor both in industry and academia. The social phenomena responsible for formation or evolution of such small groups is quite different from those for dyadic relations like friendship or large size guilds (or communities). In small groups when social actors collaborate for various tasks over time, the actors common across collaborations act as bridges which connect groups into a network of groups. Evolution of groups is affected by this network structure. Building appropriate models for this network is an important problem in the study of group evolution. This work focuses on the problem of group recurrence prediction. In order to overcome the shortcomings of two traditional group network modeling approaches: hypergraph and simplicial complex, we propose a hybrid approach: Weighted Simplicial Complex (WSC). We develop a Hasse diagram based framework to study WSCs and build several predictive models for group recurrence based on this approach. Our results demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
PublisherSpringer Verlag
Pages511-523
Number of pages13
ISBN (Print)9783319574530
DOIs
Publication statusPublished - 1 Jan 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
CountryKorea, Republic of
CityJeju
Period23/5/1726/5/17

Fingerprint

Simplicial Complex
Industry
Recurrence
Network Modeling
Predictive Model
Hybrid Approach
Hypergraph
Network Structure
Diagram
Prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sharma, A., Moore, T. J., Swami, A., & Srivastava, J. (2017). Weighted simplicial complex: A novel approach for predicting small group evolution. In Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings (pp. 511-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10234 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-57454-7_40

Weighted simplicial complex : A novel approach for predicting small group evolution. / Sharma, Ankit; Moore, Terrence J.; Swami, Ananthram; Srivastava, Jaideep.

Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Springer Verlag, 2017. p. 511-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10234 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sharma, A, Moore, TJ, Swami, A & Srivastava, J 2017, Weighted simplicial complex: A novel approach for predicting small group evolution. in Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10234 LNAI, Springer Verlag, pp. 511-523, 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017, Jeju, Korea, Republic of, 23/5/17. https://doi.org/10.1007/978-3-319-57454-7_40
Sharma A, Moore TJ, Swami A, Srivastava J. Weighted simplicial complex: A novel approach for predicting small group evolution. In Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Springer Verlag. 2017. p. 511-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-57454-7_40
Sharma, Ankit ; Moore, Terrence J. ; Swami, Ananthram ; Srivastava, Jaideep. / Weighted simplicial complex : A novel approach for predicting small group evolution. Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Springer Verlag, 2017. pp. 511-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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