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Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled “neighborhood(More)
Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, for example, "clique-members," "periphery-nodes," etc. Roles enable numerous novel and useful network-mining tasks, such as sense-making, searching for similar nodes, and node(More)
Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize(More)
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for(More)
User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However, review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake(More)
We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capable of generating the cross product of weighted/unweighted, directed/undirected, uni/bipartite graphs; (b) realistic, giving graphs that obey eleven static and dynamic laws that real graphs follow (we formally(More)
Graph clustering and graph outlier detection have been studied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with attributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer(More)
Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, our method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently(More)
Online reviews capture the testimonials of "real" people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem, with often paid spam reviewers writing fake reviews to unjustly promote or demote certain products or businesses. Existing approaches to(More)
Given a graph with node attributes, how can we find meaningful patterns such as clusters, bridges, and outliers? Attributed graphs appear in real world in the form of social networks with user interests, gene interaction networks with gene expression information, phone call networks with customer demographics, and many others. In effect, we want to group(More)