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

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

10 Citations (Scopus)

366 Downloads (Pure)

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
Subtitle of host publication	Proceedings of the World Wide Web Conference, WWW 2019
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	3151-3157
Number of pages	7
ISBN (Electronic)	9781450366748
ISBN (Print)	978-1-4503-6674-8
DOIs	https://doi.org/10.1145/3308558.3313560
Publication status	Published - 13 May 2019
Event	World Wide Web Conference, WWW 2019 - Hyatt Regency San Francisco hotel, San Francisco, United States Duration: 13 May 2019 → 17 May 2019 https://www2019.thewebconf.org/

Conference

Conference	World Wide Web Conference, WWW 2019
Abbreviated title	WWW 2019
Country/Territory	United States
City	San Francisco
Period	13/05/19 → 17/05/19
Internet address	https://www2019.thewebconf.org/

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Community detection
Information propagation
Information cascades
Network inference
Likelihood optimization

Access to Document

10.1145/3308558.3313560Licence: CC BY

2019-XProkhorenkovaTIkhonovLitvakFinal published version, 955 KBLicence: CC BY

Cite this

@inproceedings{c32c3646ef9a423a902c668c3bca3636,

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 propagation, Information cascades, Network inference, Likelihood optimization",

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

year = "2019",

month = may,

day = "13",

doi = "10.1145/3308558.3313560",

language = "English",

isbn = "978-1-4503-6674-8",

pages = "3151--3157",

booktitle = "The Web Conference 2019",

publisher = "Association for Computing Machinery",

address = "United States",

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

url = "https://www2019.thewebconf.org/",

}

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, New York, pp. 3151-3157, World Wide Web Conference, WWW 2019, San Francisco, California, 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, 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 propagation

KW - Information cascades

KW - Network inference

KW - Likelihood optimization

U2 - 10.1145/3308558.3313560

DO - 10.1145/3308558.3313560

M3 - Conference contribution

SN - 978-1-4503-6674-8

SP - 3151

EP - 3157

BT - The Web Conference 2019

PB - Association for Computing Machinery

CY - New York

T2 - World Wide Web Conference, WWW 2019

Y2 - 13 May 2019 through 17 May 2019

ER -

Learning Clusters through Information Diffusion

Abstract

Conference

UN SDGs

Keywords

Access to Document

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