Sampling Methods for Counting Temporal Motifs

Liu, Paul; Benson, Austin R.; Charikar, Moses

doi:10.1145/3289600.3290988

Cited by 54 publications

(86 citation statements)

References 65 publications

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“…The most relevant work includes methods for efficient computation of network measures, such as centrality, connectivity, density, motifs, etc. [18,27,28,32,41], as well as mining frequent subgraphs in temporal networks [44,46,49]. Path problems in temporal graphs are well studied [23,51].…”

Section: Related Workmentioning

confidence: 99%

Pattern detection in large temporal graphs using algebraic fingerprints

Thejaswi¹,

Gionis²

2020

Proceedings of the 2020 SIAM International Conference on Data Mining

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In this paper we study a family of pattern-detection problems in vertex-colored temporal graphs. In particular, given a vertex-colored temporal graph and a multi-set of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query. These types of problems have several interesting applications, for example, recommending tours for tourists, or searching for abnormal behavior in a network of financial transactions.For the family of pattern-detection problems we define, we establish complexity results and design an algebraic-algorithmic framework based on constrained multilinear sieving. We demonstrate that our solution can scale to massive graphs with up to hundred million edges, despite the problems being NP-hard. Our implementation, which is publicly available, exhibits practical edge-linear scalability and highly optimized. For example, in a real-world graph dataset with more than six million edges and a multi-set query with ten colors, we can extract an optimal solution in less than eight minutes on a haswell desktop with four cores.

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Section: Related Workmentioning

confidence: 99%

Pattern detection in large temporal graphs using algebraic fingerprints

Thejaswi¹,

Gionis²

2020

Proceedings of the 2020 SIAM International Conference on Data Mining

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“…Papers with more than 25 authors were omitted from the graph construction. reddit-reply [23,30]. Users on the social media web site reddit.com interact by commenting on each other's posts.…”

Section: Datamentioning

confidence: 99%

Retrieving Top Weighted Triangles in Graphs

Kumar

Liu

Charikar

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Pattern counting in graphs is a fundamental primitive for many network analysis tasks, and a number of methods have been developed for scaling subgraph counting to large graphs. Many real-world networks carry a natural notion of strength of connection between nodes, which are often modeled by a weighted graph, but existing scalable graph algorithms for pattern mining are designed for unweighted graphs. Here, we develop a suite of deterministic and random sampling algorithms that enable the fast discovery of the 3cliques (triangles) with the largest weight in a graph, where weight is measured by a generalized mean of a triangle's edges. For example, one of our proposed algorithms can find the top-1000 weighted triangles of a weighted graph with billions of edges in thirty seconds on a commodity server, which is orders of magnitude faster than existing "fast" enumeration schemes. Our methods thus open the door towards scalable pattern mining in weighted graphs.

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“…To verify the performance of the proposed algorithms, we first chose the baseline algorithm. In fact, there are three works on the problem of counting TGP at present, i.e., the works in [30,32] and the BT algorithm [31]. However, the work in [30] only handles the motifs with at most three edges, and the sampling framework in [32] cannot be applied to our temporal motif definition.…”

Section: Datasets and Setupmentioning

confidence: 99%

“…In fact, there are three works on the problem of counting TGP at present, i.e., the works in [30,32] and the BT algorithm [31]. However, the work in [30] only handles the motifs with at most three edges, and the sampling framework in [32] cannot be applied to our temporal motif definition. Therefore, the first two works cannot be used for the problem in this paper, thus we could only choose the BT algorithm [31] as the baseline algorithm.…”

Section: Datasets and Setupmentioning

confidence: 99%

“…Afterwards, Mackey et al [31] used the δ-temporal motif and designed a chronological edge-driven method to search all matched subgraphs of a given temporal graph. Liu et al [32] proposed a sampling framework for counting the δ-temporal motif. Since these counting methods are based on the δ-temporal motif, it is hard for them to count the temporal motif in [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New Algorithms for Counting Temporal Graph Pattern

Sun

Tan

et al. 2019

Symmetry

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Temporal networks can describe multiple types of complex systems with temporal information in the real world. As an effective method for analyzing such network, temporal graph pattern (TGP) counting has received extensive attention and has been applied in diverse domains. In this paper, we study the problem of counting the TGP in the temporal network. Then, an exact algorithm is proposed based on the time first search (TFS) algorithm. This algorithm can reduce the intermediate results generated in the graph isomorphism and has high computational efficiency. To further improve the algorithm performance, we design an estimation algorithm by applying the edge sampling strategy to the exact algorithm. Finally, we evaluate the performances of the two algorithms by counting both the symmetric and asymmetric TGP. Extensive experiments on real datasets demonstrated that the exact algorithm is faster than the existing algorithm and the estimation algorithm can greatly reduce the running time while guaranteeing the accuracy.

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Sampling Methods for Counting Temporal Motifs

Cited by 54 publications

References 65 publications

Pattern detection in large temporal graphs using algebraic fingerprints

Pattern detection in large temporal graphs using algebraic fingerprints

Retrieving Top Weighted Triangles in Graphs

New Algorithms for Counting Temporal Graph Pattern

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