Estimating Clustering Indexes in Data Streams
Identifieur interne : 001344 ( Main/Exploration ); précédent : 001343; suivant : 001345Estimating Clustering Indexes in Data Streams
Auteurs : Luciana S. Buriol [Brésil] ; Gereon Frahling [États-Unis] ; Stefano Leonardi [Italie] ; Christian Sohler [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: We present random sampling algorithms that with probability at least 1 − δ compute a (1 ±ε)-approximation of the clustering coefficient and of the number of bipartite clique subgraphs of a graph given as an incidence stream of edges. The space used by our algorithm to estimate the clustering coefficient is inversely related to the clustering coefficient of the network itself. The space used by our algorithm to compute the number K 3,3 of bipartite cliques is proportional to the ratio between the number of K 1,3 and K 3,3 in the graph. Since the space complexity depends only on the structure of the input graph and not on the number of nodes, our algorithms scale very well with increasing graph size. Therefore they provide a basic tool to analyze the structure of dense clusters in large graphs and have many applications in the discovery of web communities, the analysis of the structure of large social networks and the probing of frequent patterns in large graphs. We implemented both algorithms and evaluated their performance on networks from different application domains and of different size; The largest instance is a webgraph consisting of more than 135 million nodes and 1 billion edges. Both algorithms compute accurate results in reasonable time on the tested instances.
Url:
DOI: 10.1007/978-3-540-75520-3_55
Affiliations:
- Allemagne, Brésil, Italie, États-Unis
- Latium, Rio Grande do Sul, État de New York
- Porto Alegre, Rome
- Université fédérale du Rio Grande do Sul
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: We present random sampling algorithms that with probability at least 1 − δ compute a (1 ±ε)-approximation of the clustering coefficient and of the number of bipartite clique subgraphs of a graph given as an incidence stream of edges. The space used by our algorithm to estimate the clustering coefficient is inversely related to the clustering coefficient of the network itself. The space used by our algorithm to compute the number K 3,3 of bipartite cliques is proportional to the ratio between the number of K 1,3 and K 3,3 in the graph. Since the space complexity depends only on the structure of the input graph and not on the number of nodes, our algorithms scale very well with increasing graph size. Therefore they provide a basic tool to analyze the structure of dense clusters in large graphs and have many applications in the discovery of web communities, the analysis of the structure of large social networks and the probing of frequent patterns in large graphs. We implemented both algorithms and evaluated their performance on networks from different application domains and of different size; The largest instance is a webgraph consisting of more than 135 million nodes and 1 billion edges. Both algorithms compute accurate results in reasonable time on the tested instances.</div>
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