A scalable pattern mining approach to web graph compression with communities

Graph Compression by BFS

“…With a compression level l = 10 4 the present method yielded consistently better results than BV [3], BC [7] and Asano et al [2]. The BV highest compression scores (R = ∞) are comparable to those we obtain at level 8, while those for general usage (R = 3) are comparable to our level 4.…”

“…Buehrer and Chellapilla [7] (BC) proposed a compression based on a method presented in [10] by Feder and Motwani; They search for recurring dense bipartite graphs (communities) and for each occurrence found they generate a new node, called virtual node, that replaces the intra-links of the community (see Figure2). In [13] Karande et al showed that this method has competitive performances over well know algorithms including PageRank [14,15].…”

“…Figure 2. The method by Buehrer and Chellapilla [7] compresses a complete bipartite graph (left) by introducing a virtual node (right).…”

“…Since disk access is (by five orders of magnitude) slower than main memory access, this leads to unacceptable retrieval times. To mitigate this problem, several compression techniques have been proposed for large graphs, aimed at reducing the number of bits per link required in graph representation [1][2][3][4][5][6][7].…”

Logical Linked Data Compression

“…Work on frequent itemset mining [1,9,15,26,20,27] provides a foundation for our algorithms. [4] explored pattern mining based compression schemes for web graphs specifically designed to accomodate community queries. [25] used association rule mining techniques for generating ontology based on rdf:type statements.…”