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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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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.
Trend Mining in Dynamic Attributed Graphs
"... Abstract. Many applications see huge demands of discovering important patterns in dynamic attributed graph. In this paper, we introduce the problem of discovering trend subgraphs in dynamic attributed graphs. This new kind of pattern relies on the graph structure and the temporal evolution of the a ..."
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Abstract. Many applications see huge demands of discovering important patterns in dynamic attributed graph. In this paper, we introduce the problem of discovering trend subgraphs in dynamic attributed graphs. This new kind of pattern relies on the graph structure and the temporal evolution of the attribute values. Several interestingness measures are introduced to focus on the most relevant patterns with regard to the graph structure, the vertex attributes, and the time. We design an efficient algorithm that benefits from various constraint properties and provide an extensive empirical study from several realworld dynamic attributed graphs. 1
Cohesive Coevolution Patterns in Dynamic Attributed Graphs
"... Abstract. We focus on the discovery of interesting patterns in dynamic attributed graphs. To this end, we define the novel problem of mining cohesive coevolution patterns. Briefly speaking, cohesive coevolution patterns are trisets of vertices, timestamps, and signed attributes that describe the ..."
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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 coevolution patterns. Briefly speaking, cohesive coevolution patterns are trisets of vertices, timestamps, and signed attributes that describe the local coevolutions 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 coevolution patterns in a dynamic graph. Some experiments performed on both synthetic and realworld datasets demonstrate that our algorithm enables to discover relevant patterns in a feasible time. 1
What effects topological changes in dynamic graphs?
, 2015
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Efficient Algorithms for Extracting Frequent Episodes from Event Sequences
, 2011
"... Episode mining is one of the data mining method for timerelated data introduced by Mannila et al. in 1997. The purpose of episode mining is to extract all frequent episodes from input event sequences. Here, the episode is formulated as an acyclic labeled digraph in which labels correspond to events ..."
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Episode mining is one of the data mining method for timerelated data introduced by Mannila et al. in 1997. The purpose of episode mining is to extract all frequent episodes from input event sequences. Here, the episode is formulated as an acyclic labeled digraph in which labels correspond to events and edges represent temporal precedentsubsequent relations in an event sequence. Then, an episode gives a richer representation of temporal relationship than a subsequence, which represents just a linearly ordered relation in sequential pattern mining. For the episodes and the subclasses of episodes, several mining algorithms have been developed by several researchers. As such subclasses of episodes, Katoh et al. have introduced sectorial episodes, diamond episodes and elliptic episodes. These episodes are simpler than general episode but useful to represent the realworld information that are not represented by subsequences. The algorithms designed by Katoh et al. are levelwise; The algorithms rst nd the occurrence information of the serial episodes in an input event sequence, by