GUISE: Uniform Sampling of Graphlets for Large Graph Analysis

Bhuiyan, Mansurul; Rahman, Mahmudur; Rahman, Mahmuda; Hasan, Mohammad Al

doi:10.1109/icdm.2012.87

Cited by 94 publications

(117 citation statements)

References 17 publications

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“…These algorithms provide exact results, and here we will also concentrate on exact frequency computation, but we should note that there exist some sampling alternatives for providing approximate results. Some examples are Rand-ESU [11], Randomized g-tries [15] and GUISE [16].…”

Section: B Related Workmentioning

confidence: 99%

Parallel Subgraph Counting for Multicore Architectures

Aparício

Ribeiro

Silva

2014

2014 IEEE International Symposium on Parallel and Distributed Processing With Applications

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Computing the frequency of small subgraphs on a large network is a computationally hard task. This is, however, an important graph mining primitive, with several applications, and here we present a novel multicore parallel algorithm for this task. At the core of our methodology lies a state-of-the-art data structure, the g-trie, which represents a collection of subgraphs and allows for a very efficient sequential search. Our implementation was done using Pthreads and can run on any multicore personal computer. We employ a diagonal work sharing strategy to dynamically and effectively divide work among threads during the execution. We assess the performance of our Pthreads implementation on a set of representative networks from various domains and with diverse topological features. For most networks, we obtain a speedup of over 50 for 64 cores and an almost linear speedup up to 32 cores, showcasing the flexibility and scalability of our algorithm. This paves the way for the usage of such counting algorithms on larger subgraph and network sizes without the obligatory access to a cluster.

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Section: B Related Workmentioning

confidence: 99%

Parallel Subgraph Counting for Multicore Architectures

Aparício

Ribeiro

Silva

2014

2014 IEEE International Symposium on Parallel and Distributed Processing With Applications

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“…Known techniques for subgraph counting with large queries (e.g. [7,26]) employ similar graph traversal techniques, making PS consistent with the state of the art for subgraph counting as well as color coding.…”

Section: Procedures 2: Computing Projection Table Formentioning

confidence: 94%

“…Our query benchmark consists of the ten real world queries shown in Figure 8. The queries were derived from prior network analysis work spanning diverse domains: dros, ecoli1, ecoli2, brain1, brain2, brain3 -biological networks [22,19]; glet1, glet2 -graphlets [7]; wiki -collaboration networks [32]; youtubespam networks [24].…”

Section: Methodsmentioning

confidence: 99%

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Subgraph Counting: Color Coding Beyond Trees

Chakaravarthy

Kapralov

Murali

et al. 2016

2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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The problem of counting occurrences of query graphs in a large data graph, known as subgraph counting, is fundamental to several domains such as genomics and social network analysis. Many important special cases (e.g. triangle counting) have received significant attention. Color coding is a very general and powerful algorithmic technique for subgraph counting. Color coding has been shown to be effective in several applications, but scalable implementations are only known for the special case of tree queries (i.e. queries of treewidth one).In this paper we present the first efficient distributed implementation for color coding that goes beyond tree queries: our algorithm applies to any query graph of treewidth 2. Since tree queries can be solved in time linear in the size of the data graph, our contribution is the first step into the realm of colour coding for queries that require superlinear running time in the worst case. This superlinear complexity leads to significant load balancing problems on graphs with heavy tailed degree distributions. Our algorithm structures the computation to work around high degree nodes in the data graph, and achieves very good runtime and scalability on a diverse collection of data and query graph pairs as a result. We also provide theoretical analysis of our algorithmic techniques, showing asymptotic improvements in runtime on random graphs with power law degree distributions, a popular model for real world graphs.

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“…Recently, there is an increased interest in sampling and other heuristic approaches for obtaining approximate counts of various graphlets (Bhuiyan, Rahman, Rahman and Al Hasan, 2012;Gonen and Shavitt, 2009). However, our approach focuses on exact graphlet counting and thus sampling methods are outside the scope of this paper.…”

Section: Related Workmentioning

confidence: 99%

Graphlet decomposition: framework, algorithms, and applications

et al. 2016

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Abstract. From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of graphlets to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting graphlets of size k = {3, 4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed graphlet counting algorithms leverages a number of proven combinatorial arguments for different graphlets. For each edge, we count a few graphlets, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our graphlet counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.

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GUISE: Uniform Sampling of Graphlets for Large Graph Analysis

Cited by 94 publications

References 17 publications

Parallel Subgraph Counting for Multicore Architectures

Parallel Subgraph Counting for Multicore Architectures

Subgraph Counting: Color Coding Beyond Trees

Graphlet decomposition: framework, algorithms, and applications

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