Mining Graph Evolution Rules

Berlingerio, Michele; Bonchi, Francesco; Bringmann, Björn; Gionis, Aristides

doi:10.1007/978-3-642-04180-8_25

Cited by 130 publications

(117 citation statements)

References 21 publications

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“…Recently, such methods were proposed for the extraction of different kinds of patterns or rules in dynamic graphs (see for instance [3,8,12,18,20]). This work investigates a new direction in dynamic graph mining.…”

Section: Resultsmentioning

confidence: 99%

“…An other way to characterize a graph is the extraction of rules. Berlingerio et al [3] introduce graph evolution rules based on frequency time patterns and in [18], the authors propose multidimensional association rules. [24] studies how a graph is structurally transformed through time.…”

Section: Related Workmentioning

confidence: 99%

“…On one hand, it is possible to study the evolution of specific properties (e.g., the diameter). On the other hand, it makes sense to look at local patterns and it provides a large spectra of approaches to characterize the evolution of graphs with association rules [3,18], transformation rules [24] or other types of patterns [6,12,13,15,20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

et al. 2012

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Abstract. We focus on the discovery of interesting patterns in dynamic attributed graphs. To this end, we define the novel problem of mining cohesive co-evolution patterns. Briefly speaking, cohesive co-evolution patterns are tri-sets of vertices, timestamps, and signed attributes that describe the local co-evolutions of similar vertices at several timestamps according to set of signed attributes that express attributes trends. We design the first algorithm to mine the complete set of cohesive co-evolution patterns in a dynamic graph. Some experiments performed on both synthetic and real-world datasets demonstrate that our algorithm enables to discover relevant patterns in a feasible time.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

et al. 2012

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“…They thoroughly explored the influence of various structural features on community evolution. Another work [9] proposed to derive graph-evolution rules from frequent patterns adopting a classical association rules framework.…”

Section: Dynamic Community Detectionmentioning

confidence: 99%

Discovering Dynamic Communities in Interaction Networks

Rozenshtein

Tatti

Gionis

2014

Machine Learning and Knowledge Discovery in Databases

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Very often online social networks are defined by aggregating information regarding the interaction between the nodes of the network. For example, a call graph is defined by considering an edge for each pair of individuals who have called each other at least once -or at least k times. Similarly, an implicit social network in a social-media site is defined by considering an edge for each pair of users who have interacted in some way, e.g., have made a conversation, commented to each other's content, etc. Despite the fact that this type of definitions have been used to obtain a lot of insights regarding the structure of social networks, it is obvious that they suffer from a severe limitation: they neglect the precise time that the interaction between network nodes occurs.In this thesis we propose to study interaction networks, where one considers not only the underlying topology of the social network, but also the exact time instances that nodes interact. In an interaction network an edge is associated with a time stamp, and multiple edges may occur for the same pair of nodes. Consequently, interaction networks offer a more fine-grained representation that can be used to reveal otherwise hidden dynamic phenomena in the network.In the context of interaction networks, we study the problem of discovering communities, which are dense in terms of the underlying network structure, and whose edges occur in short time intervals. Such communities represent groups of individuals who interact with each other in some specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones. We prove that the problem we define is NP-hard, and we provide effective algorithms by adapting techniques used to find dense subgraphs. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our concepts and the good performance of our algorithms.

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“…The design of effective graph mining methods to discover actionable insights in such graphs is therefore a current challenge, to derive new knowledge about the underlying rules that govern networks (Sun and Han 2012). The last decade has witnessed intense growth in the analysis of dynamic graphs, especially from two main research tracks: (a) the study of the properties that describe the topology of the graph (de Melo et al 2011;Tong et al 2008), and (b) the extraction of specific subgraphs to describe the graph evolution (Berlingerio et al 2009;Robardet 2009;You et al 2009). Surprisingly, the simultaneous consideration of the dynamics of the graph structure and the additional vertex and edge properties has not been given much attention.…”

Section: Introductionmentioning

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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Mining Graph Evolution Rules

Cited by 130 publications

References 21 publications

Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

Cohesive Co-evolution Patterns in Dynamic Attributed Graphs

Discovering Dynamic Communities in Interaction Networks

Exceptional contextual subgraph mining

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