Mining most frequently changing component in evolving graphs

Yang, Yajun; Yu, Jeffrey Xu; Gao, Hong; Pei, Jian; Li, Jianzhong

doi:10.1007/s11280-013-0204-x

Cited by 42 publications

(26 citation statements)

References 32 publications

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“…sequences of timeconserved patterns on consecutive time. Yang et al (2013) devise an algorithm to identify the most frequently changing component. You et al (2009) compute graph rewriting rules that describe the evolution between consecutive graphs.…”

Section: Related Workmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a χ 2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

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Section: Related Workmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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“…These changes can be represented by the actions: add edge or remove edge. In some cases, the evolution of a network is modeled by edge evolution only, where the network nodes are constant over time [14]. .…”

Section: A Topological Evolutionmentioning

confidence: 99%

“…Varieties of this category have been proposed in Rossi's model [20], FVF [21], and Yang's model [14]. This category models dynamic graphs as a sequence of snapshot…”

Section: B Sequence Of Snapshotsmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Survey on Dynamic Graph Models

Zaki¹,

Attia²,

Hegazy³

et al. 2016

ijacsa

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Abstract-Most of the critical real-world networks are continuously changing and evolving with time. Motivated by the growing importance and widespread impact of this type of networks, the dynamic nature of these networks have gained a lot of attention. Because of their intrinsic and special characteristics, these networks are best represented by dynamic graph models. To cope with their evolving nature, the representation model must keep the historical information of the network along with its temporal time. Storing such amount of data, poses many problems from the perspective of dynamic graph data management. This survey provides an in-depth overview on dynamic graph related problems. Novel categorization and classification of the state of the art dynamic graph models are also presented in a systematic and comprehensive way. Finally, we discuss dynamic graph processing including the output representation of its algorithms.

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“…This has been followed by the development of an algorithm [7] for mining regular periodic patterns in dynamic networks. Subsequently, for obtaining greater insight into the evolving nature of dynamic networks, a method [8] for finding the most frequently changing components (MFCC) in evolving graphs has been presented.…”

Section: Introductionmentioning

confidence: 99%

Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

Thakur¹,

Gupta²,

Jain³

et al. 2017

IJITCS

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Abstract-Interactions appearing regularly in a network may be disturbed due to the presence of noise or random occurrence of events at some timestamps. Ignoring them may devoid us from having better understanding of the networks under consideration. Therefore, to solve this problem, researchers have attempted to find quasi/quasiregular patterns in non-weighted dynamic networks. To the best of our knowledge, no work has been reported in mining such patterns in weighted dynamic networks. So, in this paper we present a novel method which mines maximal quasi regular patterns on structure (MQRPS) and maximal quasi regular patterns on weight (MQRPW) in weighted dynamic networks. Also, we have provided a relationship between MQRPW and MQRPS which facilitates in the running of the proposed method only once, even when both are required and thus leading to reduction in computation time. Further, the analysis of the patterns so obtained is done to gain a better insight into their nature using four parameters, viz. modularity, cliques, most commonly used centrality measures and intersection. Experiments on Enron-email and a synthetic dataset show that the proposed method with relationship and analysis is potentially useful to extract previously unknown vital information.

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Mining most frequently changing component in evolving graphs

Cited by 42 publications

References 32 publications

Exceptional contextual subgraph mining

Exceptional contextual subgraph mining

Comprehensive Survey on Dynamic Graph Models

Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

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