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Frequent Subgraph Mining in Outerplanar Graphs
 PROC. 12TH ACM SIGKDD INT. CONF. ON KNOWLEDGE DISCOVERY AND DATA MINING
, 2006
"... In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases ..."
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Cited by 39 (7 self)
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In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we consider the class of outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for outerplanar graphs, and show that it works in incremental polynomial time for the practically relevant subclass of wellbehaved outerplanar graphs, i.e., which have only polynomially many simple cycles. We evaluate the algorithm empirically on chemo and bioinformatics applications.
A.: Mining graph evolution rules
 In: ECML/PKDD
, 2009
"... Abstract. In this paper we introduce graphevolution rules, a novel type of frequencybased pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. ..."
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Cited by 36 (4 self)
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Abstract. In this paper we introduce graphevolution rules, a novel type of frequencybased pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose frequency is larger than a minimum support threshold. Then, similar to the classical association rules framework, we derive graphevolution rules from frequent patterns that satisfy a given minimum confidence constraint. We discuss merits and limits of alternative definitions of support and confidence, justifying the chosen framework. To evaluate our approach we devise GERM (Graph Evolution Rule Miner), an algorithm to mine all graphevolution rules whose support and confidence are greater than given thresholds. The algorithm is applied to analyze four large realworld networks (i.e., two social networks, and two coauthorship networks from bibliographic data), using different time granularities. Our extensive experimentation confirms the feasibility and utility of the presented approach. It further shows that different kinds of networks exhibit different evolution rules, suggesting the usage of these local patterns to globally discriminate different kind of networks. 1
A Survey of Frequent Subgraph Mining Algorithms
 THE KNOWLEDGE ENGINEERING REVIEW
, 2004
"... Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplica ..."
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Cited by 27 (1 self)
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Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.
ConstraintBased Mining of Sets of Cliques Sharing Vertex Properties
 In ACNE’10
, 2010
"... Abstract. We consider data mining methods on large graphs where a set of labels is associated to each vertex. A typical example of such graphs is a social network of collaborating researchers where additional information represent the main publication targets (preferred conferences or journals) for ..."
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Cited by 4 (0 self)
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Abstract. We consider data mining methods on large graphs where a set of labels is associated to each vertex. A typical example of such graphs is a social network of collaborating researchers where additional information represent the main publication targets (preferred conferences or journals) for each author. We investigate the extraction of sets of dense subgraphs such that the vertices in all subgraphs of a set share a large enough set of labels. As a first step, we consider here the special case of dense subgraphs that are cliques. We proposed a method to compute all maximal homogeneous clique sets that satisfy userdefined constraints on the number of separated cliques, on the size of the cliques, and on the number of labels shared by all the vertices. The empirical validation illustrates the scalability of our approach and it provides experimental feedback on two real datasets, more precisely an annotated social network derived from the DBLP database and an enriched biological network of proteinprotein interactions. In both cases, we discuss the relevancy of extracted patterns thanks to available domain knowledge.
Discovering Descriptive Rules in Relational Dynamic Graphs
"... Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the socalled relational dynamic oriented graphs that can be encoded as nary relations with n ≥ 3 and thus represented by Boolean tensors. Two dimensio ..."
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Cited by 3 (3 self)
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Graph mining methods have become quite popular and a timely challenge is to discover dynamic properties in evolving graphs or networks. We consider the socalled relational dynamic oriented graphs that can be encoded as nary relations with n ≥ 3 and thus represented by Boolean tensors. Two dimensions are used to encode the graph adjacency matrices and at least one other denotes time. We design the pattern domain of multidimensional association rules, i.e., non trivial extensions of the popular association rules that may involve subsets of any dimensions in their antecedents and their consequents. First, we design new objective interestingness measures for such rules and it leads to different approaches for measuring the rule confidence. Second, we must compute collections of a priori interesting rules. It is considered here as a postprocessing of the closed patterns that can be extracted efficiently from Boolean tensors. We propose optimizations to support both rule extraction scalability and non redundancy. We illustrate the addedvalue of this new data mining task to discover patterns from a reallife relational dynamic graph.