Exceptional contextual subgraph mining

Abstract

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.

SIAS-miner: mining subjectively interesting attributed subgraphs

“…In this supervised setting, each community is treated as a target that can be assessed by well-established measures, like WRAcc. In [12], the authors aim at discovering contextualized subgraphs that are exceptional with respect to a model of the data. Restrictions on the attributes, that are associated to edges, are used to generate subgraphs.…”

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

“…The problem of discovering exceptional subgroups based on the definition of a complex target model has been widely investigated in the recent years [17,21,8,7,18,13]. Interestingly, de Sá et al [6] use a similar matrix model to support the discovery of subgroups of individuals whose preference relation between ranked objects deviates from the norm.…”

“…Bosc et al [4] propose a method to handle multi-label data where the number of labels per objects is much lower than the total number of labels which prevent the use of usual EMM model. Other dynamic EMM approaches aim to discover exceptional attributed sub-graphs [13,3].…”

“…The more different is the model, the more exceptional is the subgroup. Many models have been investigated in the last decade [21,8,7,13,6]. However, no model in the EMM framework makes it possible to characterize collection of individuals whose pairwise agreement exceptionally deviates according to a subset of objects.…”

MinerLSD: efficient mining of local patterns on attributed networks

“…The analysis of complex networks, e.g., by investigating structural properties and identifying interesting patterns, is an important task to make sense of such networks, in order to ultimately enable an understanding of their phenomena and structures, e.g., (Newman 2003;Kumar et al 2006;Almendral et al 2007;Mitzlaff et al 2011;Mitzlaff et al 2013;Atzmueller 2014;Pool et al 2014;Galbrun et al 2014;Mitzlaff et al 2014;Kibanov et al 2014;Soldano et al 2015;Atzmueller et al 2016;Bendimerad et al 2016;Kaytoue et al 2017;Atzmueller 2017;2019). In this context, data mining on such networks represented as attributed graphs has recently emerged as a prominent research topic, e.g., (Moser et al 2009;Atzmueller 2014;Galbrun et al 2014;Soldano et al 2015;Atzmueller et al 2016;Bendimerad et al 2016;Kaytoue et al 2017). Methods for mining attributed graphs focus on the identification and extraction of patterns using topological information as well as compositional information on nodes and/or edges given by a set of attributes, e.g., (Atzmueller 2018;Wasserman and Faust 1994).…”