Learning clusters through information diffusion

Liudmila Prokhorenkova; Alexey Tikhonov; Nelly Litvak

doi:10.1145/3308558.3313560

Learning clusters through information diffusion

Liudmila Prokhorenkova, Alexey Tikhonov, Nelly Litvak

Stochastic Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

8 Citations (Scopus)

Abstract

When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.

Original language	English
Title of host publication	The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	3151-3157
Number of pages	7
ISBN (Electronic)	978-1-4503-6674-8
DOIs	https://doi.org/10.1145/3308558.3313560
Publication status	Published - 13 May 2019
Event	2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019

Conference

Conference	2019 World Wide Web Conference, WWW 2019
Country/Territory	United States
City	San Francisco
Period	13/05/19 → 17/05/19

Keywords

Community detection
Information cascades
Information propagation
Likelihood optimization
Network inference

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3308558.3313560

Cite this

@inproceedings{9ea8797e23ff42a988bcdd98fc017828,

title = "Learning clusters through information diffusion",

abstract = "When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.",

keywords = "Community detection, Information cascades, Information propagation, Likelihood optimization, Network inference",

author = "Liudmila Prokhorenkova and Alexey Tikhonov and Nelly Litvak",

year = "2019",

month = may,

day = "13",

doi = "10.1145/3308558.3313560",

language = "English",

pages = "3151--3157",

booktitle = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",

}

Prokhorenkova, L, Tikhonov, A & Litvak, N 2019, Learning clusters through information diffusion. in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, New York, pp. 3151-3157, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 13/05/19. https://doi.org/10.1145/3308558.3313560

Learning clusters through information diffusion. / Prokhorenkova, Liudmila; Tikhonov, Alexey; Litvak, Nelly.
The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. New York: Association for Computing Machinery, Inc, 2019. p. 3151-3157.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Learning clusters through information diffusion

AU - Prokhorenkova, Liudmila

AU - Tikhonov, Alexey

AU - Litvak, Nelly

PY - 2019/5/13

Y1 - 2019/5/13

N2 - When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.

AB - When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.

KW - Community detection

KW - Information cascades

KW - Information propagation

KW - Likelihood optimization

KW - Network inference

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U2 - 10.1145/3308558.3313560

DO - 10.1145/3308558.3313560

M3 - Conference contribution

AN - SCOPUS:85066907369

SP - 3151

EP - 3157

BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

PB - Association for Computing Machinery, Inc

CY - New York

T2 - 2019 World Wide Web Conference, WWW 2019

Y2 - 13 May 2019 through 17 May 2019

ER -

Learning clusters through information diffusion

Abstract

Conference

Keywords

UN SDGs

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