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  1. Proceedings of Workshop on GRAph Data management Experiences and Systems (GRADES'14)
  2. How community-like is the structure of synthetically generated graphs?
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Graph Processing on an "almost" Relational Database
MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs
HelP: High-level Primitives For Large-Scale Graph Processing
Asymmetry in Large-Scale Graph Analysis, Explained
PGX.ISO: Parallel and Efficient In-Memory Engine for Subgraph Isomorphism
Using semijoin programs to solve traversal queries in graph databases
How community-like is the structure of synthetically generated graphs?
Graph Pattern Matching: Do We Have to Reinvent the Wheel?
GRATIN: Accelerating Graph Traversals in Main-Memory Column Stores
A Highly Efficient Runtime and Graph Library for Large Scale Graph Analytics
Towards a Query-by-Example System for Knowledge Graphs
Toward Representation Independent Similarity Search Over Graphs

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How community-like is the structure of synthetically generated graphs?

Content Provider ACM Digital Library
Author Dominguez-Sal, David Prat-Pérez, Arnau
Abstract Social-like graph generators have become an indispensable tool when designing proper evaluation methodologies for social graph applications, algorithms and systems. Existing synthetic generators have been designed to produce data with characteristics similar to those found in real graphs, such as power-law degree distributions, a large clustering coefficient or a small diameter. However, real social networks are organized into higher level structures, called communities, that are not explicitly considered by these generators. In this paper, we study the statistical features of the community structure found in real social networks, and compare them to those generated by the LFR and LDBC-DG generators. We found that communities show multimodal features, and thus are hard to generate with simple community models. According to our results LDBC-DG draws realistic community distributions, even reproducing the multimodality observed.
Starting Page 1
Ending Page 9
Page Count 9
File Format PDF
ISBN 9781450329828
DOI 10.1145/2621934.2621942
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-06-22
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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