Graph Mining Methods Research Articles

Coupling Graphs, Efficient Algorithms and B-Cell Epitope Prediction.

Coupling graphs are newly introduced in this paper to meet many application needs particularly in the field of bioinformatics. A coupling graph is a two-layer graph complex, in which each node from one layer of the graph complex has at least one connection with the nodes in the other layer, and vice versa. The coupling graph model is sufficiently powerful to capture strong and inherent associations between subgraph pairs in complicated applications. The focus of this paper is on mining algorithms of frequent coupling subgraphs and bioinformatics application. Although existing frequent subgraph mining algorithms are competent to identify frequent subgraphs from a graph database, they perform poorly on frequent coupling subgraph mining because they generate many irrelevant subgraphs. We propose a novel graph transformation technique to transform a coupling graph into a generic graph. Based on the transformed coupling graphs, existing graph mining methods are then utilized to discover frequent coupling subgraphs. We prove that the transformation is precise and complete and that the restoration is reversible. Experiments carried out on a database containing 10,511 coupling graphs show that our proposed algorithm reduces the mining time very much in comparison with the existing subgraph mining algorithms. Moreover, we demonstrate the usefulness of frequent coupling subgraphs by applying our algorithm to make accurate predictions of epitopes in antibody-antigen binding.

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 so-called relational dynamic oriented graphs that can be encoded as n-ary 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 multi-dimensional 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 post-processing 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 added-value of this new data mining task to discover patterns from a real-life relational dynamic graph.

Rough center mining algorithm of rough cognitive map

For the large-scale systems with complex causal connection in practical problems, fusion rough cognitive maps and graph mining methods, designed a rough central area mining algorithm of rough cognitive maps to complete the pruning problem of rough cognitive map, simplified the structure of rough cognitive map to facilitate the further relations. First of all, from the perspective of the arc, give another definition of rough cognitive map, introduce the concept of rough centrality R accuracy; second, define certainty -possible class road, according to the given threshold to determine the rough central area. Finally, design an effective rough central area mining algorithm for further analysis and forecast the rough cognitive map.

GBoost: a mathematical programming approach to graph classification and regression

Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm.

ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns

AbstractIn this paper, we introduce the concept of α‐orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are α‐orthogonal if their similarity is bounded above by α. Each α‐orthogonal pattern is also a representative for those patterns that are at least β similar to it. Given user defined α, β ∈ [0, 1], the goal is to mine an α‐orthogonal, β‐representative set that minimizes the set of unrepresented patterns.We present ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns. ORIGAMI first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. ORIGAMI then extracts an α‐orthogonal, β‐representative set from the mined maximal patterns. We show the effectiveness of our algorithm on a number of real and synthetic datasets. In particular, we show that our method is able to extract high‐quality patterns even in cases where existing enumerative graph mining methods fail to do so. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008

Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis

アプリオリ型グラフマイニング法とは相関ルール探索におけるアプリオリアルゴリズムをグラフ探索に適用するように拡張したものである。その際、k次のラティスレベルには、k個の頂点を有するグラフの中から、与えられた最小サポート値以上のものが置かれる。本方法により、”IF 事例中にサブグラフGa が存在するならば、THEN サブグラフGa∪Gb もその事例中にある確信度で存在する”というルールを導くことができる。本研究においては、事例を構成するグラフとして、化学構造式および分子の各種性質を表現する仮想的な孤立グラフ頂点を与えることにより、構造活性相関関係を表現するルールを得ることができる。本方法を230種の芳香族ニトロ化合物群における変異原性の解析に適用した結果、有効な作業仮説を得ることができた。本方法は、一般的な構造活性相関研究の方法論として採用できるものである。方法の原理と応用結果について述べる。