Fast Computation of Dense Temporal Subgraphs

Ma, Shuai; Hu, Ruifeng; Wang, Luoshu; Lin, Xuelian; Huai, Jinpeng

doi:10.1109/icde.2017.95

Cited by 49 publications

(23 citation statements)

References 26 publications

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“…Another is to build snapshots for the time-dependent graph, that is, the time-dependent graph is considered as an ordered pair of disjoint sets [9,49,80]. A time-dependent graph is denoted by G = (V, A) , where A is a set of time-dependent edges.…”

Section: Models For Discrete Time-dependent Graphsmentioning

confidence: 99%

“…The problem of finding a set of vertices with certain features and closely interacting with each other in graphs is called a subgraph mining problem. A time-dependent graph mining problem seeks to mine subgraph structures with timedependent information, such as relaxed moving object clusters [47], heavy subgraphs [7], diversified subgraphs [79], and dense subgraphs [49].…”

Section: Time-dependent Subgraph Miningmentioning

confidence: 99%

“…MEDEM is based on a filter-and-verification framework, but it not adapted to the time-dependent graph with large number of vertices, edges, and timestamps. Because there are a large number of time intervals that need to be filtered and verified, Ma et al [49] proposed a highly efficient data-driven approach named as FIDES. They showed that all the edges evolve in a convergent manner and defined a cohesive density curve to find the dense subgraph.…”

Section: Time-dependent Subgraph Miningmentioning

confidence: 99%

See 2 more Smart Citations

Time-Dependent Graphs: Definitions, Applications, and Algorithms

Wang¹,

Yuan²,

Ma³

et al. 2019

Data Sci. Eng.

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A time-dependent graph is, informally speaking, a graph structure dynamically changes with time. In such graphs, the weights associated with edges dynamically change over time, that is, the edges in such graphs are activated by sequences of time-dependent elements. Many real-life scenarios can be better modeled by time-dependent graphs, such as bioinformatics networks, transportation networks, and social networks. In particular, the time-dependent graph is a very broad concept, which is reflected in the related research with many names, including temporal graphs, evolving graphs, time-varying graphs, historical graphs, and so on. Though static graphs have been extensively studied, for their time-dependent generalizations, we are still far from a complete and mature theory of models and algorithms. In this paper, we discuss the definition and topological structure of time-dependent graphs, as well as models for their relationship to dynamic systems. In addition, we review some classic problems on time-dependent graphs, e.g., route planning, social analysis, and subgraph problem (including matching and mining). We also introduce existing time-dependent systems and summarize their advantages and limitations. We try to keep the descriptions consistent as much as possible and we hope the survey can help practitioners to understand existing time-dependent techniques.

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Section: Models For Discrete Time-dependent Graphsmentioning

confidence: 99%

Section: Time-dependent Subgraph Miningmentioning

confidence: 99%

Section: Time-dependent Subgraph Miningmentioning

confidence: 99%

See 1 more Smart Citation

Time-Dependent Graphs: Definitions, Applications, and Algorithms

Wang¹,

Yuan²,

Ma³

et al. 2019

Data Sci. Eng.

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show abstract

“…Performing threat hunting through graph computations establishes new programmability requirements beyond existing graph programming platforms [12,65] (discussed in Section 2). Automation based on existing threat hunting practices [8,13,27,32,33,43,66,67], graph-based forensics [53,63,90], and temporal graph retrieval development [56,93,99] inspired the design of τ -calculus.…”

Section: Dynamic Threat Model Approachesmentioning

confidence: 99%

Threat Intelligence Computing

Shu

Araujo

Schales

et al. 2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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Cyber threat hunting is the process of proactively and iteratively formulating and validating threat hypotheses based on securityrelevant observations and domain knowledge. To facilitate threat hunting tasks, this paper introduces threat intelligence computing as a new methodology that models threat discovery as a graph computation problem. It enables efficient programming for solving threat discovery problems, equipping threat hunters with a suite of potent new tools for agile codifications of threat hypotheses, automated evidence mining, and interactive data inspection capabilities. A concrete realization of a threat intelligence computing platform is presented through the design and implementation of a domain-specific graph language with interactive visualization support and a distributed graph database. The platform was evaluated in a two-week DARPA competition for threat detection on a test bed comprising a wide variety of systems monitored in real time. During this period, sub-billion records were produced, streamed, and analyzed, dozens of threat hunting tasks were dynamically planned and programmed, and attack campaigns with diverse malicious intent were discovered. The platform exhibited strong detection and analytics capabilities coupled with high efficiency, resulting in a leadership position in the competition. Additional evaluations on comprehensive policy reasoning are outlined to demonstrate the versatility of the platform and the expressiveness of the language.

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“…The studies of behavior mining in temporal networks are related to our work. Besides the aforementioned relevant research [3,6,7], Ma et al [5] devised a dense subgraph mining algorithm to identify cohesive subgraphs in a temporal network. Li et al [4] addressed the problem of mining periodic behaviors for moving objects.…”

Section: Introductionmentioning

confidence: 99%

Seasonal-Periodic Subgraph Mining in Temporal Networks

Zhang

Guo

Zhao

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Seasonal periodicity is a frequent phenomenon for social interactions in temporal networks. A key property of this behavior is that it exhibits periodicity for multiple particular periods in temporal networks. Mining such seasonal-periodic patterns is significant since it can indicate interesting relationships between the individuals involved in the interactions. Unfortunately, most previous studies for periodic pattern mining ignore the seasonal feature. This motivates us to explore mining seasonal-periodic subgraphs, and the investigation presents a novel model, called maximal σ-periodic ω-seasonal k-subgraph. It represents a subgraph with size larger than k and that appears at least σ times periodically in at least ω particular periods on the temporal graph. Since seasonal-periodic patterns do not satisfy the anti-monotonic property, we propose a weak version of support measure with an anti-monotonic property to reduce the search space efficiently. Then, we present an effective mining algorithm to seek all maximal σ-periodic ω-seasonal k-subgraphs. Experimental results on real-life datasets show the effectiveness and efficiency of our approach. CCS CONCEPTS • Mathematics of computing → Graph algorithms; • Information systems → Data mining; • Theory of computation → Graph algorithms analysis.

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Fast Computation of Dense Temporal Subgraphs

Cited by 49 publications

References 26 publications

Time-Dependent Graphs: Definitions, Applications, and Algorithms

Time-Dependent Graphs: Definitions, Applications, and Algorithms

Threat Intelligence Computing

Seasonal-Periodic Subgraph Mining in Temporal Networks

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