Time-Evolving Relational Classification and Ensemble Methods

Rossi, Ryan A.; Neville, Jennifer

doi:10.1007/978-3-642-30217-6_1

Cited by 25 publications

(17 citation statements)

References 17 publications

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“…There has been some work on modeling temporal events in large scale networks [2,31] and other work that uses temporal link and attribute patterns to improve predictive models [26]. In addition, there is work on identifying clusters in dynamic data [5,27] but these methods focus on discovering underlying communities over time-sets of nodes that are densely connected together.…”

mentioning

confidence: 99%

Modeling dynamic behavior in large evolving graphs

Rossi

Gallagher

Neville

et al. 2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

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116

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Given a large time-evolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the "roles" of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/datadriven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable and computationally efficient. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

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mentioning

confidence: 99%

Modeling dynamic behavior in large evolving graphs

Rossi

Gallagher

Neville

et al. 2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

Self Cite

142

116

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“…Nevertheless, the Role-Dynamics approach is a prime candidate for other applications such as real-time graphbased anomaly detection [18], dynamic relational classification [22], and for predicting future structural patterns. The goal of anomaly detection in graphs is to detect nodes, links, or network states that are anomalous, and therefore the actual interpretation of the learned patterns from Role-Dynamics are no longer important (or of secondary importance for forensics).…”

Section: Discussionmentioning

confidence: 99%

Role-dynamics

Rossi

Gallagher

Neville

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

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To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural "role" dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are nonstationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.

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“…Having an extensive database containing time-related records, we are able to evaluate timeconsistent models on different timestamps and time windows. [12] suggests that temporal weighting -the decay of importance of past information -and temporal granularity -the use of time windows -form the basis for any arbitrary transformation with respect to the temporal information. More concretely, for each preferred timestamp a snapshot of the database and the network can be taken such that it describes the true situation on that specific timestamp.…”

Section: Modeling Approachmentioning

confidence: 99%

Using social network knowledge for detecting spider constructions in social security fraud

Vlasselaer

Meskens

Dromme

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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As social networks offer a vast amount of additional information to enrich standard learning algorithms, the most challenging part is extracting relevant information from networked data. Fraudulent behavior is imperceptibly concealed both in local and relational data, making it even harder to define useful input for prediction models. Starting from expert knowledge, this paper succeeds to efficiently incorporate social network effects to detect fraud for the Belgian governmental social security institution, and to improve the performance of traditional non-relational fraud prediction tasks. As there are many types of social security fraud, this paper concentrates on payment fraud, predicting which companies intentionally disobey their payment duties to the government. We introduce a new fraudulent structure, the so-called spider constructions, which can easily be translated in terms of social networks and included in the learning algorithms. Focusing on the egonet of each company, the proposed method can handle large scale networks. In order to face the skewed class distribution, the SMOTE approach is applied to rebalance the data. The models were trained on different timestamps and evaluated on varying time windows. Using techniques as Random Forest, logistic regression and Naive Bayes, this paper shows that the combined relational model improves the AUC score and the precision of the predictions in comparison to the base scenario where only local variables are used.

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Time-Evolving Relational Classification and Ensemble Methods

Cited by 25 publications

References 17 publications

Modeling dynamic behavior in large evolving graphs

Modeling dynamic behavior in large evolving graphs

Role-dynamics

Using social network knowledge for detecting spider constructions in social security fraud

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