Scalable information flow mining in networks

Karthik Subbian, Chidananda Sridhar, Charu C. Aggarwal, Jaideep Srivastava

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

1 Citation (Scopus)

Abstract

The problem of understanding user activities and their patterns of communication is extremely important in social and collaboration networks. This can be achieved by tracking the dominant content flow trends and their interactions between users in the network. Our approach tracks all possible paths of information flow using its network structure, content propagated and the time of propagation. We also show that the complexity class of this problem is #P-complete. Because most social networks have many activities and interactions, it is inevitable the proposed method will be computationally intensive. Therefore, we propose an efficient method for mining information flow patterns, especially in large networks, using distributed vertex-centric computational models. We use the Gather-Apply-Scatter (GAS) paradigm to implement our approach. We experimentally show that our approach achieves over three orders of magnitude advantage over the state-of-the-art, with an increasing advantage with a greater number of cores. We also study the effectiveness of the discovered content flow patterns by using it in the context of an influence analysis application.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages130-146
Number of pages17
Volume8726 LNAI
EditionPART 3
ISBN (Print)9783662448441
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy
Duration: 15 Sep 201419 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8726 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014
CityNancy
Period15/9/1419/9/14

Fingerprint

Information Flow
Flow patterns
Mining
Flow Pattern
Influence Analysis
Vertex Model
Distributed Networks
Complexity Classes
Scatter
Interaction
Network Structure
Computational Model
Social Networks
Communication
Paradigm
Propagation
Path

Keywords

  • Influence Analysis Network-centric approach
  • Information Flow Mining
  • Scalable Influence Analysis
  • Vertex-centric models

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Subbian, K., Sridhar, C., Aggarwal, C. C., & Srivastava, J. (2014). Scalable information flow mining in networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8726 LNAI, pp. 130-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-662-44845-8_9

Scalable information flow mining in networks. / Subbian, Karthik; Sridhar, Chidananda; Aggarwal, Charu C.; Srivastava, Jaideep.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8726 LNAI PART 3. ed. Springer Verlag, 2014. p. 130-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3).

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

Subbian, K, Sridhar, C, Aggarwal, CC & Srivastava, J 2014, Scalable information flow mining in networks. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8726 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8726 LNAI, Springer Verlag, pp. 130-146, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Nancy, 15/9/14. https://doi.org/10.1007/978-3-662-44845-8_9
Subbian K, Sridhar C, Aggarwal CC, Srivastava J. Scalable information flow mining in networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8726 LNAI. Springer Verlag. 2014. p. 130-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-662-44845-8_9
Subbian, Karthik ; Sridhar, Chidananda ; Aggarwal, Charu C. ; Srivastava, Jaideep. / Scalable information flow mining in networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8726 LNAI PART 3. ed. Springer Verlag, 2014. pp. 130-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{960de7bc96cc4a58821ef1056ead0476,
title = "Scalable information flow mining in networks",
abstract = "The problem of understanding user activities and their patterns of communication is extremely important in social and collaboration networks. This can be achieved by tracking the dominant content flow trends and their interactions between users in the network. Our approach tracks all possible paths of information flow using its network structure, content propagated and the time of propagation. We also show that the complexity class of this problem is #P-complete. Because most social networks have many activities and interactions, it is inevitable the proposed method will be computationally intensive. Therefore, we propose an efficient method for mining information flow patterns, especially in large networks, using distributed vertex-centric computational models. We use the Gather-Apply-Scatter (GAS) paradigm to implement our approach. We experimentally show that our approach achieves over three orders of magnitude advantage over the state-of-the-art, with an increasing advantage with a greater number of cores. We also study the effectiveness of the discovered content flow patterns by using it in the context of an influence analysis application.",
keywords = "Influence Analysis Network-centric approach, Information Flow Mining, Scalable Influence Analysis, Vertex-centric models",
author = "Karthik Subbian and Chidananda Sridhar and Aggarwal, {Charu C.} and Jaideep Srivastava",
year = "2014",
doi = "10.1007/978-3-662-44845-8_9",
language = "English",
isbn = "9783662448441",
volume = "8726 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 3",
pages = "130--146",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

T1 - Scalable information flow mining in networks

AU - Subbian, Karthik

AU - Sridhar, Chidananda

AU - Aggarwal, Charu C.

AU - Srivastava, Jaideep

PY - 2014

Y1 - 2014

N2 - The problem of understanding user activities and their patterns of communication is extremely important in social and collaboration networks. This can be achieved by tracking the dominant content flow trends and their interactions between users in the network. Our approach tracks all possible paths of information flow using its network structure, content propagated and the time of propagation. We also show that the complexity class of this problem is #P-complete. Because most social networks have many activities and interactions, it is inevitable the proposed method will be computationally intensive. Therefore, we propose an efficient method for mining information flow patterns, especially in large networks, using distributed vertex-centric computational models. We use the Gather-Apply-Scatter (GAS) paradigm to implement our approach. We experimentally show that our approach achieves over three orders of magnitude advantage over the state-of-the-art, with an increasing advantage with a greater number of cores. We also study the effectiveness of the discovered content flow patterns by using it in the context of an influence analysis application.

AB - The problem of understanding user activities and their patterns of communication is extremely important in social and collaboration networks. This can be achieved by tracking the dominant content flow trends and their interactions between users in the network. Our approach tracks all possible paths of information flow using its network structure, content propagated and the time of propagation. We also show that the complexity class of this problem is #P-complete. Because most social networks have many activities and interactions, it is inevitable the proposed method will be computationally intensive. Therefore, we propose an efficient method for mining information flow patterns, especially in large networks, using distributed vertex-centric computational models. We use the Gather-Apply-Scatter (GAS) paradigm to implement our approach. We experimentally show that our approach achieves over three orders of magnitude advantage over the state-of-the-art, with an increasing advantage with a greater number of cores. We also study the effectiveness of the discovered content flow patterns by using it in the context of an influence analysis application.

KW - Influence Analysis Network-centric approach

KW - Information Flow Mining

KW - Scalable Influence Analysis

KW - Vertex-centric models

UR - http://www.scopus.com/inward/record.url?scp=84907012505&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907012505&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-44845-8_9

DO - 10.1007/978-3-662-44845-8_9

M3 - Conference contribution

SN - 9783662448441

VL - 8726 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 130

EP - 146

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -