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Hancock: a language for extracting signatures from data streams
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Using relational knowledge discovery to prevent securities fraud
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Data streams: algorithms and applications
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Relational Dependency Networks
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Classification in Networked Data: A Toolkit and a Univariate Case Study
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Relational data pre-processing techniques for improved securities fraud detection
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Exploiting time-varying relationships in statistical relational models
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Mobile call graphs: beyond power-law and lognormal distributions
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Users in Volatile Communities: Studying Active Participation and Community Evolution
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Fast mining of complex time-stamped events
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Improving learning in networked data by combining explicit and mined links
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Scalable proximity estimation and link prediction in online social networks
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Relevance feedback using weight propagation compared with information-theoretic query expansion
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Autocorrelation and linkage cause bias in evaluation of relational learners
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Tracking hidden groups using communications
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Computationally efficient scoring of activity using demographics and connectivity of entities
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Strange bedfellows: community identification in bittorrent
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Surprising patterns for the call duration distribution of mobile phone users
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A hybrid fraud scoring and spike detection technique in streaming data
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ACM Transactions on Intelligent Systems and Technology (TIST)
Cellular telephony and the question of privacy
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Mining large distributed log data in near real time
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On clustering techniques for change diagnosis in data streams
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Relevance feedback using weight propagation
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ARDEN: Anonymous networking in delay tolerant networks
Ad Hoc Networks
Community-based analysis of netflow for early detection of security incidents
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Isolating and analyzing fraud activities in a large cellular network via voice call graph analysis
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Time-Evolving relational classification and ensemble methods
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining dense structures to uncover anomalous behaviour in financial network data
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FastLane: improving performance of software transactional memory for low thread counts
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Competence region modelling in relational classification
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AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Early security classification of skype users via machine learning
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Discovery of emergent malicious campaigns in cellular networks
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AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Expert Systems with Applications: An International Journal
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We consider problems that can be characterized by large dynamic graphs. Communication networks provide the prototypical example of such problems where nodes in the graph are network IDs and the edges represent communication between pairs of network IDs. In such graphs, nodes and edges appear and disappear through time so that methods that apply to static graphs are not sufficient. We introduce a data structure that captures, in an approximate sense, the graph and its evolution through time. The data structure arises from a bottom-up representation of the large graph as the union of small subgraphs, called Communities of Interest (COI), centered on every node. These subgraphs are interesting in their own right and we discuss two applications in the area of telecommunications fraud detection to help motivate the ideas.