Techniques for Graph Analytics on Big Data

Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines and genome sequencing. The field of graph pattern matching has been of significant importance and has wide-spread applications. Conceptually,...

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Veröffentlicht in:Proceedings (IEEE International Congress on Big Data) S. 255 - 262
Hauptverfasser: Nisar, M. Usman, Fard, Arash, Miller, John A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2013
Schlagworte:
Computational modeling
Data handling
Data models
Distributed algorithms
Global Positioning System
graph analytics; big data; graph simulation; parallel and distributed algorithms
Information management
Pattern matching
ISSN:2379-7703
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Zusammenfassung:Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines and genome sequencing. The field of graph pattern matching has been of significant importance and has wide-spread applications. Conceptually, we want to find subgraphs that match a pattern in a given graph. Much work has been done in this field with solutions like Subgraph Isomorphism and Regular Expression matching. With Big Data, scientists are frequently running into massive graphs that have amplified the challenge that this area poses. We study the speedup and communication behavior of three distributed algorithms for inexact graph pattern matching. We also study the impact of different graph partitionings on runtime and network I/O. Our extensive results show that the algorithms exhibit excellent scalable behavior and min-cut partitioning can lead to improved performance under some circumstances, and can drastically reduce the network traffic as well.
ISSN:2379-7703
DOI:10.1109/BigData.Congress.2013.78