A joint classification method to integrate scientific and social networks

Mahmood Neshati; Ehsaneddin Asgari; Djoerd Hiemstra; Hamid Beigy

doi:10.1007/978-3-642-36973-5_11

A joint classification method to integrate scientific and social networks

Mahmood Neshati, Ehsaneddin Asgari, Djoerd Hiemstra, Hamid Beigy

Databases (Former)

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

8 Citations (Scopus)

30 Downloads (Pure)

Abstract

In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

Original language	Undefined
Title of host publication	35th European Conference on IR Research, ECIR 2013
Place of Publication	Berlin
Publisher	Springer
Pages	122-133
Number of pages	12
ISBN (Print)	978-3-642-36972-8
DOIs	https://doi.org/10.1007/978-3-642-36973-5_11
Publication status	Published - Mar 2013
Event	35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation Duration: 24 Mar 2013 → 27 Mar 2013 Conference number: 35

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	7814
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	35th European Conference on Information Retrieval, ECIR 2013
Abbreviated title	ECIR
Country/Territory	Russian Federation
City	Moscow
Period	24/03/13 → 27/03/13

Keywords

EWI-24059
METIS-300201
IR-88459

Access to Document

10.1007/978-3-642-36973-5_11

ecir2013social

Cite this

@inproceedings{f533231872624f41883cbe6cdbfeeb04,

title = "A joint classification method to integrate scientific and social networks",

abstract = "In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.",

keywords = "EWI-24059, METIS-300201, IR-88459",

author = "Mahmood Neshati and Ehsaneddin Asgari and Djoerd Hiemstra and Hamid Beigy",

note = "eemcs-eprint-24059 ; 35th European Conference on Information Retrieval, ECIR 2013 ; Conference date: 24-03-2013 Through 27-03-2013",

year = "2013",

month = mar,

doi = "10.1007/978-3-642-36973-5_11",

language = "Undefined",

isbn = "978-3-642-36972-8",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "122--133",

booktitle = "35th European Conference on IR Research, ECIR 2013",

address = "Germany",

}

Neshati, M, Asgari, E, Hiemstra, D & Beigy, H 2013, A joint classification method to integrate scientific and social networks. in 35th European Conference on IR Research, ECIR 2013. Lecture Notes in Computer Science, vol. 7814, Springer, Berlin, pp. 122-133, 35th European Conference on Information Retrieval, ECIR 2013, Moscow, Russian Federation, 24/03/13. https://doi.org/10.1007/978-3-642-36973-5_11

A joint classification method to integrate scientific and social networks. / Neshati, Mahmood; Asgari, Ehsaneddin; Hiemstra, Djoerd et al.
35th European Conference on IR Research, ECIR 2013. Berlin: Springer, 2013. p. 122-133 (Lecture Notes in Computer Science; Vol. 7814).

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

TY - GEN

T1 - A joint classification method to integrate scientific and social networks

AU - Neshati, Mahmood

AU - Asgari, Ehsaneddin

AU - Hiemstra, Djoerd

AU - Beigy, Hamid

N1 - Conference code: 35

PY - 2013/3

Y1 - 2013/3

N2 - In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

AB - In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.

KW - EWI-24059

KW - METIS-300201

KW - IR-88459

U2 - 10.1007/978-3-642-36973-5_11

DO - 10.1007/978-3-642-36973-5_11

M3 - Conference contribution

SN - 978-3-642-36972-8

T3 - Lecture Notes in Computer Science

SP - 122

EP - 133

BT - 35th European Conference on IR Research, ECIR 2013

PB - Springer

CY - Berlin

T2 - 35th European Conference on Information Retrieval, ECIR 2013

Y2 - 24 March 2013 through 27 March 2013

ER -