A vertex similarity probability model for finding network community structure

Kan Li; Yin Pang

doi:10.1007/978-3-642-30217-6_38

A vertex similarity probability model for finding network community structure

Kan Li^*, Yin Pang

^*Corresponding author for this work

School of Computer Science and Technology

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

7 Citations (Scopus)

Abstract

Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, "Common neighbor index" is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Pages	456-467
Number of pages	12
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-30217-6_38
Publication status	Published - 2012
Event	16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia Duration: 29 May 2012 → 1 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7301 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Country/Territory	Malaysia
City	Kuala Lumpur
Period	29/05/12 → 1/06/12

Keywords

common neighbor index
community structure
type of the network structure
vertex similarity

Access to Document

10.1007/978-3-642-30217-6_38

Cite this

Li, K., & Pang, Y. (2012). A vertex similarity probability model for finding network community structure. In Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings (PART 1 ed., pp. 456-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7301 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-30217-6_38

Li, Kan ; Pang, Yin. / A vertex similarity probability model for finding network community structure. Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 1. ed. 2012. pp. 456-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{2888dfa91d45432db73e4dcd5fbafc73,

title = "A vertex similarity probability model for finding network community structure",

abstract = "Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, {"}Common neighbor index{"} is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.",

keywords = "common neighbor index, community structure, type of the network structure, vertex similarity",

author = "Kan Li and Yin Pang",

year = "2012",

doi = "10.1007/978-3-642-30217-6_38",

language = "English",

isbn = "9783642302169",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "456--467",

booktitle = "Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings",

edition = "PART 1",

note = "16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 ; Conference date: 29-05-2012 Through 01-06-2012",

}

Li, K & Pang, Y 2012, A vertex similarity probability model for finding network community structure. in Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7301 LNAI, pp. 456-467, 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012, Kuala Lumpur, Malaysia, 29/05/12. https://doi.org/10.1007/978-3-642-30217-6_38

A vertex similarity probability model for finding network community structure. / Li, Kan; Pang, Yin.
Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 1. ed. 2012. p. 456-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7301 LNAI, No. PART 1).

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

TY - GEN

T1 - A vertex similarity probability model for finding network community structure

AU - Li, Kan

AU - Pang, Yin

PY - 2012

Y1 - 2012

N2 - Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, "Common neighbor index" is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.

AB - Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, "Common neighbor index" is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.

KW - common neighbor index

KW - community structure

KW - type of the network structure

KW - vertex similarity

UR - http://www.scopus.com/inward/record.url?scp=84861442811&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-30217-6_38

DO - 10.1007/978-3-642-30217-6_38

M3 - Conference contribution

AN - SCOPUS:84861442811

SN - 9783642302169

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

SP - 456

EP - 467

BT - Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings

T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012

Y2 - 29 May 2012 through 1 June 2012

ER -

Li K, Pang Y. A vertex similarity probability model for finding network community structure. In Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 1 ed. 2012. p. 456-467. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-30217-6_38

A vertex similarity probability model for finding network community structure

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this