Network similarity via multiple social theories

Berlingerio, Michele; Koutra, Danai; Eliassi-Rad, Tina; Faloutsos, Christos

doi:10.1145/2492517.2492582

Cited by 67 publications

(83 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, a method that compares random walks from two networks is wellcorrelated with a method that simply measures density. (3) We show that two methods -namely, NetSimile [6] and Random Walk with Restarts -are consistently close to the consensus. Second, we apply our approaches to a set of longitudinal datasets.…”

Section: Introductionmentioning

confidence: 79%

“…Vector-based NetSimile [6] first calculates 7 local structural features for each node (backed by various social theories). It then calculates the median and the first four moments of distribution for each feature.…”

Section: Network Similarity Methodsmentioning

confidence: 99%

“…Vector-based Eigenvalues (Eigs) calculates the k largest eigenvalues for each network. As in [6], we used k = 10. This defines a length-k feature vector.…”

Section: Network Similarity Methodsmentioning

confidence: 99%

“…Previous work [6] identified an anomalous network in each of these datasets, which upon examination, proved to correspond to important real-world events such as the Iranian presidential elections exhibited online in the Twitter Retweets graph. For each of these datasets, we use these anomalous networks as the reference networks.…”

Section: Experiments On Longitudinal Datamentioning

confidence: 97%

“…There are many network-similarity algorithms that require known node-correspondences (e.g., DeltaCon [12] and most edit-distance based methods). Others do not require known node-correspondence (e.g., NetSimile [6] and graphlet-based approaches [16]). …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Guide to Selecting a Network Similarity Method

Soundarajan¹,

Eliassi-Rad²,

Gallagher³

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

Self Cite

View full text Add to dashboard Cite

We consider the problem of determining how similar two networks (without known node-correspondences) are. This problem occurs frequently in real-world applications such as transfer learning and change detection. Many networksimilarity methods exist; and it is unclear how one should select from amongst them. We provide the first empirical study on the relationships between different networksimilarity methods. Specifically, we present (1) an approach for identifying groups of comparable network-similarity methods and (2) an approach for computing the consensus among a given set of network-similarity methods. We compare and contrast twenty network-similarity methods by applying our approaches to a variety of real datasets spanning multiple domains. Our experiments demonstrate that (1) different network-similarity methods are surprisingly well correlated, (2) some complex network-similarity methods can be closely approximated by a much simpler method, and (3) a few network-similarity methods produce rankings that are very close to the consensus ranking.

show abstract

Section: Introductionmentioning

confidence: 79%

Section: Network Similarity Methodsmentioning

confidence: 99%

“…Vector-based Eigenvalues (Eigs) calculates the k largest eigenvalues for each network. As in [6], we used k = 10. This defines a length-k feature vector.…”

Section: Network Similarity Methodsmentioning

confidence: 99%

Section: Experiments On Longitudinal Datamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Guide to Selecting a Network Similarity Method

Soundarajan¹,

Eliassi-Rad²,

Gallagher³

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

Self Cite

View full text Add to dashboard Cite

show abstract

Anomaly detection in dynamic networks: a survey

Ranshous

Shen

Koutra

et al. 2015

WIREs Computational Stats

Self Cite

293

191

View full text Add to dashboard Cite

Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state-of-the-art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two-stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two-tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories-community, compression, decomposition, distance, and probabilistic model based-we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches.

show abstract