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  1. Proceedings of Workshop on GRAph Data management Experiences and Systems (GRADES'14)
  2. Toward Representation Independent Similarity Search Over 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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Toward Representation Independent Similarity Search Over Graphs

Content Provider ACM Digital Library
Author Chodpathumwan, Yodsawalai Sun, Yizhou Aleyasin, Amirhossein Picado, Jose Termehchy, Arash
Abstract Finding similar entities over data graphs is an important problem with many applications. Current similarity search algorithms use intuitively appealing heuristics that leverage the link information in the data graph to quantify the degree of similarity between its entities. In this paper, using examples from real-world data sets, we show that people represent the same information using data graphs with different shapes. We argue that in order for a similarity search algorithm to be usable and effective, it should be representation independent: it should return essentially the same answers for a query over different graphs that represent the same information. We formalize this property and show that the outcome of current similarity search algorithms depend highly on data representation. Hence, they may be effective on some datasets and ineffective over others. We also perform an empirical study and analyze the sensitivity of current methods against changes in data representation. Our results indicate that the output of these algorithms are highly affected by changes in data representation.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450329828
DOI 10.1145/2621934.2621946
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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