DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors

Günnemann, Stephan; Boden, Brigitte; Seidl, Thomas

doi:10.1007/978-3-642-23780-5_46

Cited by 49 publications

(34 citation statements)

References 18 publications

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“…In NetScan, like in other partitioning algorithms, the number of clusters must be known, but this point has been relaxed in recent works [25]. Recently, other approaches have also been introduced in order to detect dense subgraphs which are also homogeneous for the attributes [26], [27]. Dang et al have extended the Newman's modularity by adding a term to measure the attribute-based similarity between two nodes [23].…”

Section: State Of the Artmentioning

confidence: 96%

Combining Relations and Text in Scientific Network Clustering

Combe

Largeron

Egyed-Zsigmond

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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In this paper, we present different combined clustering methods and we evaluate their performances and their results on a dataset with ground truth. This dataset, built from several sources, contains a scientific social network in which textual data is associated to each vertex and the classes are known. Indeed, while the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the vertices and attributes describing them, is quite new. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.

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Section: State Of the Artmentioning

confidence: 96%

Combining Relations and Text in Scientific Network Clustering

Combe

Largeron

Egyed-Zsigmond

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…PLSA-PHITS [4], Community-User-Topic model [15] and PMC [16] are three representatives in this category. Other fusing the content and structure methods, such as SA-clustering [17] via augmenting the underlying network to take into account the content information, heuristic algorithm CKC [18] to solve the connected k-Center problem, subspace clustering algorithm [19] on graphs with feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Detecting Topic-oriented Overlapping Community Using Hybrid Hypergraph Model

Shen¹,

Yang

Sun

2016

INT J COMPUT COMMUN

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A large number of emerging information networks brings new challenges to the overlapping community detection. The meaningful community should be topicoriented. However, the topology-based methods only reflect the strength of connection, but ignore the consistency of the topics. This paper explores a topic-oriented overlapping community detection method for information work. The method utilizes a hybrid hypergraph model to combine the node content and structure information naturally. Two connections for hyperedge pair, including real connection and virtual connection are defined. A novel hyperedge pair similarity measure is proposed by combining linearly extended common neighbors metric for real connection and incremental fitness for virtual connection. Extensive experiments on two real-world datasets validate our proposed method outperforms other baseline algorithms.

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“…Subspace Outlier Detection (SOD) [25] is a subspace LOF method which combines the tasks of finding relevant subspaces and detecting outliers, but the problem of indexing in high-dimensional space is not addressed. DB-CSC [15] combines graph clustering with subspace feature selection on the numeric attributes of the graphs, but experimental datasets only have synthetic data up to 20 dimensions, so it is not clear if this approach scales to higher dimensions. Subspace feature selection in high-dimensional data remains an active area for research.…”

Section: Numeric Outlier Detectionmentioning

confidence: 99%

Finding the most descriptive substructures in graphs with discrete and numeric labels

2013

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Many graph datasets are labelled with discrete and numeric attributes. Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure and the distribution of attribute values and propose an outlier-detection step, which is used as a constraint during substructure discovery. By pruning anomalous vertices and edges, more weight is given to the most descriptive substructures. Our method is applicable to multi-dimensional numeric attributes; we outline how it can be extended for highdimensional data. We support our findings with experiments on transaction graphs and single large graphs from the domains of physical building security and digital forensics, measuring the effect on runtime, memory requirements and coverage of discovered patterns, relative to the unconstrained approach.

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DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors

Cited by 49 publications

References 18 publications

Combining Relations and Text in Scientific Network Clustering

Combining Relations and Text in Scientific Network Clustering

Detecting Topic-oriented Overlapping Community Using Hybrid Hypergraph Model

Finding the most descriptive substructures in graphs with discrete and numeric labels

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