Abstract
Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose effective outlier edge detection algorithm. The proposed algorithms are inspired by community structures that are very common in social networks. We found that the graph structure around an edge holds critical information for determining the authenticity of the edge. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. More important, by analyzing the authenticity of the edges in a graph, we are able to reveal underlying structure and properties of a graph. Thus, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: (1) a preprocessing tool that improves the performance of graph clustering algorithms; (2) an outlier node detection algorithm; and (3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques. They also address the importance of analyzing the edges in graph mining—a topic that has been mostly neglected by researchers.
Highlights
Graphs are an important data representation, which have been extensively used in many scientific fields such as data mining, bioinformatics, multimedia content retrieval and computer vision
We evaluate the performance of the proposed outlier edge detection algorithms
We introduce outlier edge detection algorithms based on two random graph
Summary
Graphs are an important data representation, which have been extensively used in many scientific fields such as data mining, bioinformatics, multimedia content retrieval and computer vision. The edges with low authentic scores, which are called weak links in this paper, are likely to be outliers. We evaluated the outlier edge detection algorithm that is based on the authentic score using injected edges in real-world graph data. Akoglu et al detected outlier nodes using the near-cliques and stars, heavy vicinities and dominant heavy links properties of the ego-network- the induced network formed by a focal node and its direct neighbors [12] They observed that some pairs of the features of normal nodes follow a power law and defined an outlier score function that measures the deviation of a node from the normal patterns. Detection of missing edges (or link prediction) is the opposite technique of outlier edge detection These algorithms find missing edges between pairs of nodes in a graph. In practice, these similarity scores do not give satisfactory performance if one uses them to detect outlier edges
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