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Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay

Authors:

Shaunak Chatterjee,

Rómer RosalesAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1855 - 1864

https://doi.org/10.1145/2939672.2939814

Published: 13 August 2016 Publication History

Abstract

Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking.

In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.

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Cited By

Ko JJung J(2023)Understanding asymmetric synergistic effect between movie actorsPLOS ONE10.1371/journal.pone.028461318:4(e0284613)Online publication date: 20-Apr-2023
https://doi.org/10.1371/journal.pone.0284613
Wang JWu LZhao HJia N(2023)Multi-view enhanced zero-shot node classificationInformation Processing & Management10.1016/j.ipm.2023.10347960:6(103479)Online publication date: Nov-2023
https://doi.org/10.1016/j.ipm.2023.103479
Yue QLiang JCui JBai LZhang ARangwala H(2022)Dual Bidirectional Graph Convolutional Networks for Zero-shot Node ClassificationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539316(2408-2417)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539316
Show More Cited By

Index Terms

Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay

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Published In

cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 August 2016

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Ko JJung J(2023)Understanding asymmetric synergistic effect between movie actorsPLOS ONE10.1371/journal.pone.028461318:4(e0284613)Online publication date: 20-Apr-2023
https://doi.org/10.1371/journal.pone.0284613
Wang JWu LZhao HJia N(2023)Multi-view enhanced zero-shot node classificationInformation Processing & Management10.1016/j.ipm.2023.10347960:6(103479)Online publication date: Nov-2023
https://doi.org/10.1016/j.ipm.2023.103479
Yue QLiang JCui JBai LZhang ARangwala H(2022)Dual Bidirectional Graph Convolutional Networks for Zero-shot Node ClassificationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539316(2408-2417)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539316
Lo Cde Longueau ESaha AChatterjee S(2020)Edge formation in Social Networks to Nurture Content CreatorsProceedings of The Web Conference 202010.1145/3366423.3380267(1999-2008)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380267
Sun YWang SHsieh TTang XHonavar V(2019)MEGANProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367531(3527-3533)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367531
Gamage ARappaport BAeron SHu X(2019)Common Randomized Shortest Paths (C-RSP): A Simple Yet Effective Framework for Multi-view Graph EmbeddingICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8683424(3542-3546)Online publication date: May-2019
https://doi.org/10.1109/ICASSP.2019.8683424
Shi YChan PZhuang HGui HHan JMatwin SYu SFarooq F(2017)PRePProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3097990(425-434)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3097990

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