Neighbor query friendly compression of social networks

Maserrat, Hossein; Pei, Jian

doi:10.1145/1835804.1835873

Cited by 70 publications

(69 citation statements)

References 19 publications

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“…Boldi [14] studied the compression of web graphs using the lexicographic localities; Chierichetti et al [5] extended it to the social networks; Apostolico et al [24] used BFS based method for compression. Maserrat et al [25] used multi-position linearizations for better serving neighborhood queries. Our SLASHBURN is the first work to take the powerlaw characteristic of most real world graphs into advantage for addressing the 'no good cut' problem and graph compression.…”

Section: Related Workmentioning

confidence: 99%

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

Kang

Faloutsos

2011

2011 IEEE 11th International Conference on Data Mining

111

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Abstract-Given a real world graph, how should we layout its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find cliquelike communities, like the 'cavemen graph', and compress them. We show that the block-diagonal mental image of the 'cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively.Based on the idea, we propose the SLASHBURN method (burn the hubs, and slash the remaining graph into smaller connected components). Our view point has several advantages: (a) it avoids the 'no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools.Experimental results show that our SLASHBURN method consistently outperforms other methods on all datasets, giving good compression and faster running time.

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Section: Related Workmentioning

confidence: 99%

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

Kang

Faloutsos

2011

2011 IEEE 11th International Conference on Data Mining

111

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“…Existing methods for compressing static graphs generally use two types of strategies: (1) removing edges to simplify the overall graph [4,8], or (2) merging nodes that have similar properties (such as common neighbors) [5,6]. While the existing methods compress a static graph from its "spatial" (nodes or edges) perspective, in this paper we propose to compress a dynamic graph from both the "spatial" and the "temporal" perspectives simultaneously.…”

Section: Introductionmentioning

confidence: 99%

On compressing weighted time-evolving graphs

Liu

Kan

Chan

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Existing graph compression techniques mostly focus on static graphs. However for many practical graphs such as social networks the edge weights frequently change over time. This phenomenon raises the question of how to compress dynamic graphs while maintaining most of their intrinsic structural patterns at each time snapshot. In this paper we show that the encoding cost of a dynamic graph is proportional to the heterogeneity of a three dimensional tensor that represents the dynamic graph. We propose an effective algorithm that compresses a dynamic graph by reducing the heterogeneity of its tensor representation, and at the same time also maintains a maximum lossy compression error at any time stamp of the dynamic graph. The bounded compression error benefits compressed graphs in that they retain good approximations of the original edge weights, and hence properties of the original graph (such as shortest paths) are well preserved. To the best of our knowledge, this is the first work that compresses weighted dynamic graphs with bounded lossy compression error at any time snapshot of the graph.

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“…In contrast to lossless compression schemes (e.g., [6,9,17]), query preserving compression is relative to Q, i.e., it generates small Dc that preserves the information only relevant to queries in Q rather than preserving the entire original D. Hence it often achieves a better compression ratio than lossless compression. Indeed, this approach has proven effective in answering graph queries on large social network graphs [16,31,32] and in cryptographic applications [24]. If the compression can be conducted in PTIME [16,31,32] and moreover, queries in Q can be answered in the compressed databases Dc in parallel polylog-time, perhaps by combining with other techniques such as indexing, then Q is Π-tractable, i.e., Q ∈ ΠT 0 Q .…”

Section: (4) Lowest Common Ancestors (Lca) Consider L3mentioning

confidence: 99%

“…Indeed, this approach has proven effective in answering graph queries on large social network graphs [16,31,32] and in cryptographic applications [24]. If the compression can be conducted in PTIME [16,31,32] and moreover, queries in Q can be answered in the compressed databases Dc in parallel polylog-time, perhaps by combining with other techniques such as indexing, then Q is Π-tractable, i.e., Q ∈ ΠT 0 Q . (6) Query answering using views.…”

Section: (4) Lowest Common Ancestors (Lca) Consider L3mentioning

confidence: 99%

“…In addition to building indices as shown in the example, many more preprocessing strategies have proven effective and are being widely used in practice. These include (i) query-preserving compression schemes that preserve the answers to a class of queries rather than preserving the data itself [16,24,31,32], (ii) building views such that queries can be answered using the views without accessing the original big data [1,23,30]; and (iii) bounded incremental algorithms [35] that, after evaluating queries once on the original data (as preprocessing), incrementally evaluate the queries in response to the changes to the data such that the cost of query evaluation is a function of the size of the changes, rather than the size of the original big data [15,37].…”

Section: Introductionmentioning

confidence: 99%

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Making queries tractable on big data with preprocessing

2013

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A query class is traditionally considered tractable if there exists a polynomial-time (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME algorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data off-line, so that queries in the class can be subsequently evaluated on the data efficiently. This paper aims to provide a formal foundation for this approach in terms of computational complexity. (1) We propose a set of Π-tractable queries, denoted by ΠT 0 Q , to characterize classes of queries that can be answered in parallel poly-logarithmic time (NC) after PTIME preprocessing. (2) We show that several natural query classes are Π-tractable and are feasible on big data. (3) We also study a set ΠT Q of query classes that can be effectively converted to Π-tractable queries by re-factorizing its data and queries for preprocessing. We introduce a form of NC reductions to characterize such conversions. (4) We show that a natural query class is complete for ΠT Q . (5) We also show that ΠT 0 Q ⊂ P unless P = NC, i.e., the set ΠT 0 Q of all Π-tractable queries is properly contained in the set P of all PTIME queries. Nonetheless, ΠT Q = P, i.e., all PTIME query classes can be made Π-tractable via proper refactorizations. This work is a step towards understanding the tractability of queries in the context of big data.

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Neighbor query friendly compression of social networks

Cited by 70 publications

References 19 publications

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

On compressing weighted time-evolving graphs

Making queries tractable on big data with preprocessing

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