Mining coherent subgraphs in multi-layer graphs with edge labels

Boden, Brigitte; Günnemann, Stephan; Hoffmann, H.; Seidl, Thomas

doi:10.1145/2339530.2339726

Cited by 104 publications

(87 citation statements)

References 42 publications

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“…Even then WRAcc stays static, however, and does not change while attributes are combined, leading to patterns of lower quality. As a conclusion, those experiments show that MiMaG can hardly be adapted to solve the problem we consider here, which is quite different from the one addressed by the authors in Boden et al (2012).…”

Section: Comparison To Mimagcontrasting

confidence: 80%

“…In this approach, two vertices connected by edges from different dimension are considered to be more strongly connected, whereas in our exceptional contextual subgraphs framework, dimensions are not presupposed but inferred based on high weighted relative accuracy. In the multi-layer coherent subgraph approach called MiMag, Boden et al (2012) use numerical labels on vertices to assess edges' similarity in different layers of the graph. Vertices connected by edges with similar weights induce quasi-cliques.…”

Section: Related Workmentioning

confidence: 99%

“…2), namely the MiMaG approach (Boden et al 2012) designed for subspace clustering on layered edge-weighted graphs, and an Exceptional Model Mining algorithm (van Leeuwen and Knobbe 2012). …”

Section: Comparative Experimentsmentioning

confidence: 99%

“…2, several approaches for mining edge-attributed graphs have been proposed in the literature. The one closest in goal to our technique, an algorithm called MiMaG, has been introduced in Boden et al (2012). In that framework, graphs exist in several layers, and edges have weights that are determined by those layers, obviously a formulation that is rather close to our own.…”

Section: Comparison To Mimagmentioning

confidence: 99%

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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: Comparison To Mimagcontrasting

confidence: 80%

Section: Related Workmentioning

confidence: 99%

Section: Comparative Experimentsmentioning

confidence: 99%

Section: Comparison To Mimagmentioning

confidence: 99%

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Exceptional contextual subgraph mining

et al. 2017

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“…Such a rich graph is called multigraph and it allows different types of edges in order to represent different types of relations between vertices [1,2]. Example of multigraphs are: social networks spanning over the same set of people, but with different life aspects (e.g.…”

Section: Introductionmentioning

confidence: 99%

SuMGra: Querying Multigraphs via Efficient Indexing

Ingalalli

Ienco²,

Poncelet

2016

Lecture Notes in Computer Science

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Abstract. Many real world datasets can be represented by a network with a set of nodes interconnected with each other by multiple relations. Such a rich graph is called a multigraph. Unfortunately, all the existing algorithms for subgraph query matching are not able to adequately leverage multiple relationships that exist between the nodes. In this paper we propose an efficient indexing schema for querying single large multigraphs, where the indexing schema aptly captures the neighbourhood structure in the data graph. Our proposal SuMGra couples this novel indexing schema with a subgraph search algorithm to quickly traverse though the solution space to enumerate all the matchings. Extensive experiments conducted on real benchmarks prove the time efficiency as well as the scalability of SuMGra .

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Using triangles and latent factor cosine similarity prior to improve community detection in multi‐relational social networks

Zhan

Sun

2018

Concurrency and Computation

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Community detection is a key to understanding the structure of complex networks. Communities, or clusters, are groups of vertices having higher probability of being connected to each other than to the members in other groups. Considering the importance of triangle structures, we firstpropose -tensor to model ordinary relationships and triangle relationships simultaneously. Then, we propose a simple but effective latent factor prior, ie, latent factor cosine similarity prior, to improve community detection. The latent factor cosine similarity prior is a kind of statistics of the well-defined synthetic multi-relational social networks. It is based on a key observation that most latent feature factors of intra-group members in these networks are highly similar according to cosine similarity measure. Using this prior along with the RESCAL tensor factorization model, we can obtain a superior latent feature factor matrix. Moreover, N-RESCAL model, a variant of RESCAL model, and its corresponding algorithm N-RESCAL-ALS are proposed for the simplicity and the removal of the limit of cosine similarity. Once the latent factor matrix is obtained by factorizing -tensor using N-RESCAL model, we apply agglomerative clustering algorithm for community discovery. We call this framework as TNRA. Experiment results on several real-world datasets are surprisingly promising, clearly demonstrating the power of the proposed prior and the effectiveness of our proposed methods.

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Mining coherent subgraphs in multi-layer graphs with edge labels

Cited by 104 publications

References 42 publications

Exceptional contextual subgraph mining

Exceptional contextual subgraph mining

SuMGra: Querying Multigraphs via Efficient Indexing

Using triangles and latent factor cosine similarity prior to improve community detection in multi‐relational social networks

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