Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Citation prediction using time series approach KDD Cup 2003 (task 1)
ACM SIGKDD Explorations Newsletter
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
Estimating number of citations using author reputation
SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
Continuous Conditional Random Fields for Regression in Remote Sensing
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Who should I cite: learning literature search models from citation behavior
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Statistical Analysis and Data Mining
To better stand on the shoulder of giants
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Link prediction in citation networks
Journal of the American Society for Information Science and Technology
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When faced with the task of forming predictions for nodes in a social network, it can be quite difficult to decide which of the available connections among nodes should be used for the best results. This problem is further exacerbated when temporal information is available, prompting the question of whether this information should be aggregated or not, and if not, which portions of it should be used. With this challenge in mind, we propose a novel utilization of variograms for selecting potentially useful relationship types, whose merits are then evaluated using a Gaussian Conditional Random Field model for node attribute prediction of temporal social networks with a multigraph structure. Our flexible model allows for measuring many kinds of relationships between nodes in the network that evolve over time, as well as using those relationships to augment the outputs of various unstructured predictors to further improve performance. The experimental results exhibit the effectiveness of using particular relationships to boost performance of unstructured predictors, show that using other relationships could actually impede performance, and also indicate that while variograms alone are not necessarily sufficient to identify a useful relationship, they greatly help in removing obviously useless measures, and can be combined with intuition to identify the optimal relationships.