A new approach to the maximum flow problem
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
A fast kernel-based multilevel algorithm for graph clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Algorithm for Graph Clustering Using Minimum Cut Tree
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Clustering Via a Discrete Uncoupling Process
SIAM Journal on Matrix Analysis and Applications
Identifying hotspots on the real-time web
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
CrowdTracker: enabling community-based real-time web monitoring
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Group Profiling for Understanding Social Structures
ACM Transactions on Intelligent Systems and Technology (TIST)
Predicting semantic annotations on the real-time web
Proceedings of the 23rd ACM conference on Hypertext and social media
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based crowd retrieval on the real-time web
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.01 |
In this paper, we study the problem of automatically discovering and tracking transient crowds in highly-dynamic social messaging systems like Twitter and Facebook. Unlike the more static and long-lived group-based membership offered on many social networks (e.g., fan of the LA Lakers), a transient crowd is a short-lived ad-hoc collection of users, representing a "hotspot" on the real-time web. Successful detection of these hotspots can positively impact related research directions in online event detection, content personalization, social information discovery, etc. Concretely, we propose to model crowd formation and dispersion through a message-based communication clustering approach over time-evolving graphs that captures the natural conversational nature of social messaging systems. Two of the salient features of the proposed approach are (i) an efficient locality- based clustering approach for identifying crowds of users in near real-time compared to more heavyweight static clustering algorithms; and (ii) a novel crowd tracking and evolution approach for linking crowds across time periods. We find that the locality-based clustering approach results in empirically high-quality clusters relative to static graph clus- tering techniques at a fraction of the computational cost. Based on a three month snapshot of Twitter consisting of 711,612 users and 61.3 million messages, we show how the proposed approach can successfully identify and track interesting crowds based on the Twitter communication structure and uncover crowd-based topics of interest.