gLabTrie: A Data Structure for Motif Discovery with Constraints

Mongiovì, Misael; Micale, Giovanni; Ferro, Alfredo; Giugno, Rosalba; Pulvirenti, Alfredo; Shasha, Dennis

doi:10.1007/978-3-319-96193-4_3

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…There is rich history of work on triangle counting in static graphs. Various algorithm for triangle and motif counting in attributed graphs have also been proposed [13,30,37,41,42,56]. Here we only focus on temporal networks and refer the reader to [2] and the tutorial [44] for a more detailed list of related work.…”

Section: Related Workmentioning

confidence: 99%

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Pashanasangi

Seshadhri

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Triangle counting is a fundamental technique in network analysis, that has received much attention in various input models. The vast majority of triangle counting algorithms are targeted to static graphs. Yet, many real-world graphs are directed and temporal, where edges come with timestamps. Temporal triangles yield much more information, since they account for both the graph topology and the timestamps.Temporal triangle counting has seen a few recent results, but there are varying definitions of temporal triangles. In all cases, temporal triangle patterns enforce constraints on the time interval between edges (in the triangle). We define a general notion ( 1,3 , 1,2 , 2,3 )-temporal triangles that allows for separate time constraints for all pairs of edges.Our main result is a new algorithm, DOTTT (Degeneracy Oriented Temporal Triangle Totaler), that exactly counts all directed variants of ( 1,3 , 1,2 , 2,3 )-temporal triangles. Using the classic idea of degeneracy ordering with careful combinatorial arguments, we can prove that DOTTT runs in ( log ) time, where is the number of (temporal) edges and is the graph degeneracy (max core number). Up to log factors, this matches the running time of the best static triangle counters. Moreover, this running time is better than existing.DOTTT has excellent practical behavior and runs twice as fast as existing state-of-the-art temporal triangle counters (and is also more general). For example, DOTTT computes all types of temporal queries in Bitcoin temporal network with half a billion edges in less than an hour on a commodity machine. CCS CONCEPTS• Theory of computation → Graph algorithms analysis; • Information systems → Data mining; Social networks; • Mathematics of computing → Graph algorithms.

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Section: Related Workmentioning

confidence: 99%

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Pashanasangi

Seshadhri

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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“…The fundamental change made on gLabTries is "label-based queries". Mongioví et al [49] defined labelbased queries as quadruple Q containing multiset of labels C, requested size of motifs k, frequency threshold f , and p-value threshold (Q = (C, k, f, p)). While implementing gLabTrie, users give sets of constraints as a requirement, and the system generates topology for each specified constraints.…”

Section: Motif Discovery Algorithmsmentioning

confidence: 99%

“…gLabTries G-tries is a prefix tree data structure that facilitates the storage of a set of graphs efficiently by preventing re-use of the subgraphs information among common prefixes. Misael et al [49] proposed motif discovering algorithms for both undirected and directed networks called gLabTrie. gLabTrie is an extension to the original G-tries motif discovery algorithm [50].…”

Section: Motif Discovery Algorithmsmentioning

confidence: 99%

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Big networks: A survey

Bedru

Xiao

et al. 2020

Computer Science Review

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A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called big network. Big networks are generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further.

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“…Graphs used throughout this work are simple, have a single layer of connectivity and do not distinguish the node or edge types with qualitative or quantitative features. Therefore we do not discuss here algorithms that use colored nodes or edges [53,58,148], and neither those that consider networks that are heterogeneous [57,156], multilayer [23,143], labelled/attributed [123], probabilistic [162] or any kind of weighted graphs [197].…”

Section: Algorithms Not Consideredmentioning

confidence: 99%

A Survey on Subgraph Counting: Concepts, Algorithms and Applications to Network Motifs and Graphlets

Ribeiro,

Paredes,

Silva

et al. 2019

Preprint

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Computing subgraph frequencies is a fundamental task that lies at the core of several network analysis methodologies, such as network motifs and graphlet-based metrics, which have been widely used to categorize and compare networks from multiple domains. Counting subgraphs is however computationally very expensive and there has been a large body of work on efficient algorithms and strategies to make subgraph counting feasible for larger subgraphs and networks.This survey aims precisely to provide a comprehensive overview of the existing methods for subgraph counting. Our main contribution is a general and structured review of existing algorithms, classifying them on a set of key characteristics, highlighting their main similarities and differences. We identify and describe the main conceptual approaches, giving insight on their advantages and limitations, and provide pointers to existing implementations. We initially focus on exact sequential algorithms, but we also do a thorough survey on approximate methodologies (with a trade-off between accuracy and execution time) and parallel strategies (that need to deal with an unbalanced search space).

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gLabTrie: A Data Structure for Motif Discovery with Constraints

Cited by 6 publications

References 24 publications

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Big networks: A survey

A Survey on Subgraph Counting: Concepts, Algorithms and Applications to Network Motifs and Graphlets

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