TY - GEN
T1 - New Frontiers of Multi-Network Mining
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
AU - Du, Boxin
AU - Zhang, Si
AU - Yan, Yuchen
AU - Tong, Hanghang
N1 - Funding Information:
3 ACKNOWLEDGEMENT This work is supported by National Science Foundation under grant No. 1947135, and 2003924 and by the NSF Program on Fairness in AI in collaboration with Amazon under award No. 1939725, The content of the information in this document does not necessarily reflect the position or the policy of the Government or Amazon, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. REFERENCES
Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - Networks (i.e., graphs) are often collected from multiple sources and platforms, such as social networks extracted from multiple online platforms, team-specific collaboration networks within an organization, and inter-dependent infrastructure networks, etc. Such networks from different sources form the multi-networks, which can exhibit the unique patterns that are invisible if we mine the individual network separately. However, compared with single-network mining, multi-network mining is still under-explored due to its unique challenges. First ( multi-network models ), networks under different circumstances can be modeled into a variety of models. How to properly build multi-network models from the complex data? Second ( multi-network mining algorithms ), it is often nontrivial to either extend single-network mining algorithms to multi-networks or design new algorithms. How to develop effective and efficient mining algorithms on multi-networks? The objectives of this tutorial are to: (1) comprehensively review the existing multi-network models, (2) elaborate the techniques in multi-network mining with a special focus on recent advances, and (3) elucidate open challenges and future research directions. We believe this tutorial could be beneficial to various application domains, and attract researchers and practitioners from data mining as well as other interdisciplinary fields.
AB - Networks (i.e., graphs) are often collected from multiple sources and platforms, such as social networks extracted from multiple online platforms, team-specific collaboration networks within an organization, and inter-dependent infrastructure networks, etc. Such networks from different sources form the multi-networks, which can exhibit the unique patterns that are invisible if we mine the individual network separately. However, compared with single-network mining, multi-network mining is still under-explored due to its unique challenges. First ( multi-network models ), networks under different circumstances can be modeled into a variety of models. How to properly build multi-network models from the complex data? Second ( multi-network mining algorithms ), it is often nontrivial to either extend single-network mining algorithms to multi-networks or design new algorithms. How to develop effective and efficient mining algorithms on multi-networks? The objectives of this tutorial are to: (1) comprehensively review the existing multi-network models, (2) elaborate the techniques in multi-network mining with a special focus on recent advances, and (3) elucidate open challenges and future research directions. We believe this tutorial could be beneficial to various application domains, and attract researchers and practitioners from data mining as well as other interdisciplinary fields.
KW - future trend
KW - graph mining
KW - multi-network mining
KW - recent developments
UR - http://www.scopus.com/inward/record.url?scp=85114910589&partnerID=8YFLogxK
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U2 - 10.1145/3447548.3470801
DO - 10.1145/3447548.3470801
M3 - Conference contribution
AN - SCOPUS:85114910589
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4038
EP - 4039
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 14 August 2021 through 18 August 2021
ER -