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- Keith Henderson, Brian Gallagher, +6 authors Lei Li
- KDD
- 2012

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)

- Leman Akoglu, Hanghang Tong, Danai Koutra
- Data Mining and Knowledge Discovery
- 2014

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)

Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the nodecorrespondence problem? Analogously, how can we extract a small number of descriptive, numerical features from each graph that effectively serve as the graph’s “signature”? Having such… (More)

How much did a network change since yesterday? How different is the wiring between Bob’s brain (a lefthanded male) and Alice’s brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired… (More)

- Danai Koutra, U. Kang, Jilles Vreeken, Christos Faloutsos
- SDM
- 2014

How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the ‘importance’ of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a ‘vocabulary’ of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of… (More)

- Danai Koutra, Tai-You Ke, U. Kang, Duen Horng Chau, Hsing-Kuo Kenneth Pao, Christos Faloutsos
- ECML/PKDD
- 2011

If several friends of Smith have committed petty thefts, what would you say about Smith? Most people would not be surprised if Smith is a hardened criminal. Guilt-by-association methods combine weak signals to derive stronger ones, and have been extensively used for anomaly detection and classification in numerous settings (e.g., accounting fraud,… (More)

Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. However, for dynamic networks, no comprehensive survey exists covering the richness of methods proposed and their differences, as well as the variety of problems they solve. In light of this, the current survey focuses on… (More)

We deal with two independent but related problems, those of graph similarity and subgraph matching, which are both important practical problems useful in several fields of science, engineering and data analysis. For the problem of graph similarity, we develop and test a new framework for solving the problem using belief propagation and related ideas. For… (More)

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label the class of a node in a network based on its neighbors and appropriate assumptions of homophily (“birds of a feather flock together”) or heterophily… (More)

- Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos
- 2013 IEEE/ACM International Conference on…
- 2013

Given a set of <i>k</i> networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a "signature" for the network? Having such signatures will… (More)