Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Dynamic mixed membership blockmodel for evolving networks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Time-based sampling of social network activity graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Detecting Novel Discrepancies in Communication Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
It's who you know: graph mining using recursive structural features
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
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 non-stationary 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.