An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

Inokuchi, Akira; Washio, Takashi; Motoda, Hiroshi

doi:10.1007/3-540-45372-5_2

Cited by 762 publications

(515 citation statements)

References 6 publications

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“…This is an instance of the general issue of frequent subgraph mining [8,11,17]; however, most of the prior work in this area is focused on graphs that are richly labeled and undirected, often motivated by applications to chemical compound and bioinformatics datasets. While our data has labels as well, we are specifically interested in enumerating subgraphs based purely on their structures, so heuristics for pruning the search space using node and edge labels cannot be applied.…”

Section: Related Workmentioning

confidence: 99%

Patterns of Influence in a Recommendation Network

Leskovec

Singh

Kleinberg

2006

Advances in Knowledge Discovery and Data Mining

264

174

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Abstract. Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products. Such a dataset allows us to pose a number of fundamental questions: What kinds of cascades arise frequently in real life? What features distinguish them? We enumerate and count cascade subgraphs on large directed graphs; as one component of this, we develop a novel efficient heuristic based on graph isomorphism testing that scales to large datasets. We discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur. The relative abundance of different cascade subgraphs suggests subtle properties of the underlying social network and recommendation process.

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Section: Related Workmentioning

confidence: 99%

Patterns of Influence in a Recommendation Network

Leskovec

Singh

Kleinberg

2006

Advances in Knowledge Discovery and Data Mining

264

174

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“…Frequent subgraph mining [8,9,4] entails two significant overheads: candidate set generation and (sub)graph isomorphism checking. However, these overheads are exacerbated when the size of the graph data is substantial and the support threshold is low.…”

Section: Previous Workmentioning

confidence: 99%

“…Most research work in frequent subgraph mining [8,9,4], assumes each discovered frequent subgraph is equally important. Because of this, a lot of redundant and repetitive frequent patterns exist in the resultant set.…”

Section: Weighted Frequent Subgraph Miningmentioning

confidence: 99%

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Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach

Coenen

Zito

2010

Lecture Notes in Computer Science

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Abstract. The mining of social networks entails a high degree of computational complexity. This complexity is exacerbate when considering longitudinal social network data. To address this complexity issue three weighting schemes are proposed in this paper. The fundamental idea is to reduce the complexity by considering only the most significant nodes and links. The proposed weighting schemes have been incorporated into the weighted variations and extensions of the well established gSpan frequent subgraph mining algorithm. The focus of the work is the cattle movement network found in Great Britain. A complete evaluation of the proposed approaches is presented using this network. In addition, the utility of the discovered patterns is illustrated by constructing a sequential data set to which a sequential mining algorithm can be applied to capturing the changes in "behavior" represented by a network.

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“…Almost all techniques, however, work on so called transaction databases [1]. Not only for itemsets, but also in the case of trees [20] and graphs [12,15,19], the database consists of a collection of transactions, and a frequent pattern is discovered if it occurs in enough such transactions. Even in the multi-relational case, as considered in the WARMR system [4], the database can be seen as a collection of transactions in which each transaction consists of a small relational database.…”

Section: Introductionmentioning

confidence: 99%

Discovery and Application of Functional Dependencies in Conjunctive Query Mining

Goethals¹,

Laurent²,

Page³

2010

Data Warehousing and Knowledge Discovery

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Abstract. We present an algorithm for mining frequent queries in arbitrary relational databases, over which functional dependencies are assumed. Building upon previous results, we restrict to the simple, but appealing subclass of simple conjunctive queries. The proposed algorithm makes use of the functional dependencies of the database to optimise the generation of queries and prune redundant queries. Furthermore, our algorithm is capable of detecting previously unknown functional dependencies that hold on the database relations as well as on joins of relations. These detected dependencies are subsequently used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL, and provide several promising experimental results.

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An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

Cited by 762 publications

References 6 publications

Patterns of Influence in a Recommendation Network

Patterns of Influence in a Recommendation Network

Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach

Discovery and Application of Functional Dependencies in Conjunctive Query Mining

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