Tracking local communities in streaming graphs with a dynamic algorithm

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which find...

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Vydané v:Social network analysis and mining Ročník 6; číslo 1; s. 65
Hlavní autori: Zakrzewska, Anita, Bader, David A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Vienna Springer Vienna 01.12.2016
Springer Nature B.V
Predmet:
Algorithms
Apexes
Applications of Graph Theory and Complex Networks
Community
Computation
Computer Science
Data
Data Mining and Knowledge Discovery
Economics
Game Theory
Graph theory
Graphical representations
Graphs
Greedy algorithms
Humanities
Latency
Law
Massive data points
Methodology of the Social Sciences
Methods
Modularity
Original Article
Seeds
Social and Behav. Sciences
Social networks
Statistics for Social Sciences
Tracking
Static Algorithm
Dynamic Algorithm
Fitness Score
Community Detection
Batch Size
ISSN:1869-5450, 1869-5469
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Popis
Shrnutí:A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed vertices. Greedy, agglomerative algorithms, which are commonly used in seed set expansion, have been previously designed only for a static, unchanging graph. However, in many applications, new data are constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which maintains a local community over time by incrementally updating as the underlying graph changes. We show that our dynamic algorithm outputs high-quality communities that are similar to those found when using a standard static algorithm. It works well both when beginning with an already existing graph and in the fully streaming case when starting with no data. The dynamic approach is also faster than re-computation when low latency updates are needed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1869-5450
1869-5469
DOI:10.1007/s13278-016-0374-5