Multi-label Relational Neighbor Classification using Social Context Features

1. Multi-label Relational Neighbor Classification using Social Context Features Xi Wang and Gita Sukthankar Department of EECS University of Central Florida

2. Motivation • The conventional relational classification model focuses on the single-label classification problem. • Real-world relational datasets contain instances associated with multiple labels. • Connections between instances in multi-label networks are driven by various casual reasons. Artificial Intelligence Data Mining Machine Learning Example: Scientific collaboration network

3. Problem Formulation • Node classification in multi-relational networks • Input: • Network structure (i.e., connectivity information) • Labels of some actors in the network • Output: • Labels of the other actors

4. Classification in Networked Data • Homophily: nodes with similar labels are more likely to be connected • Markov assumption: • The label of one node depends on that of its immediate neighbors in the graph • Relational models are built based on the labels of neighbors. • Predictions are made using collective inference.

5. Contribution • A new multi-label iterative relational neighbor classifier (SCRN) • Extract social context features using edge clustering to represent a node’s potential group membership • Use of social features boosts classification performance overbenchmarks on several real-world collaborative networked datasets

6. Relational Neighbor Classifier • The Relational Neighbor (RN) classifier proposed by Macskassy et al. (MRDM’03), is a simple relational probabilistic model that makes predictions for a given node based solely on the class labels of its neighbors. Training Graph Iteration 2 Iteration 1

7. Relational Neighbor Classifier • Weighted-vote relational neighbor classifier (wvRN) estimates prediction probability as: Here is the usual normalization factor, and isthe weight of the link between node and

8. Apply RN in Multi-relational Network :nodes with both labels (red, green) : nodes with green label only : nodes with red label only Ground truth

9. Edge-Based Social Feature Extraction • Connections in human networks are mainly affiliation-driven. • Since each connection can often be regarded as principally resulting from one affiliation, links possess a strong correlation with a single affiliation class. • The edge class information is not readily available in most social media datasets, but an unsupervised clustering algorithm can be applied to partition the edges into disjoint sets(KDD’09,CIKM’09).

10. Clusteredges using K-Means • Scalable edge clustering method proposed by Tang et al. (CIKM’09). • Each edge isrepresentedin a feature-based format, where each edge is characterized by its adjacent nodes. • K-means clustering is used to separate the edges into groups, and the social feature (SF) vector is constructed based on edge cluster IDs. Originalnetwork Step1:Edgerepresentations Step2:Constructsocialfeatures

11. Edge-ClusteringVisualization Figure:Asubset of DBLP with 95 instances. Edges are clustered into 10 groups,with eachshown in a different color.

12. Proposed Method:SCRN • The initial set of reference features for class c can be defined as the weighted sum of social feature vectors for nodes known to be in class c: • Then node ’s class propagation probability for class c conditioned on its social features:

13. SCRN • SCRN estimates the class-membership probability of node belonging to class c using the following equation: classpropagationprobability similaritybetweenconnectednodes (link weight) classprobabilityofitsneighbors

14. SCRNOverview Input: , Max_Iter Output: for nodes in • Construct nodes’ social feature space • Initialize the class reference vectors for each class • Calculate the class-propagation probability for each test node • Repeat until # of iterations > Max_Iter or predictions converge • Estimate test node’s class probability • Update the test node’s class probability in collective inference • Update the class reference vectors • Re-calculate each node’s class-propagation probability

15. SCRNVisualization Figure:SCRN on syntheticmulti-labelnetworkwith1000nodes and 32 classes (15 iterations).

16. Datasets • DBLP • We construct a weighted collaboration network for authors who have published at least 2 papers during the 2000 to 2010 time- frame. • We selected 15 representative conferences in 6 research areas:

17. Datasets • IMDb • We extract movies and TV shows released between 2000 and 2010, and those directed by the same director are linked together. • We only retain movies and TV programs with greater than 5 links. • Each movie can be assigned to a subset of 27 different candidate movie genres in the database such as “Drama", “Comedy", “Documentary" and “Action”.

18. Datasets • YouTube • Asubset of data (15000 nodes) from the original YouTube dataset[1]usingsnowballsampling. • Each user in YouTube can subscribe to different interest groups and add other users as his/her contacts. • Classlabelsare47 interest groups. [1]http://www.public.asu.edu/~ltang9/social_ dimension.html

19. ComparativeMethods • Edge (EdgeCluster) • wvRN • Prior • Random

20. ExperimentSetting • Sizeofsocialfeaturespace: • 1000forDBLPandYouTube;10000forIMDb • Classpropagationprobabilityiscalculatedwith theGeneralizedHistogramIntersectionKernel. • Relaxation Labeling is used in the collective inference framework for SCRN and wvRN. • Weassumethenumberoflabelsfortestingnodesisknown.

21. Experiment Setting • We employ the network cross-validation (NCV) method (KAIS’11) to reduce the overlap between test samples. • Classificationperformanceisevaluatedbasedon Micro-F1, Macro-F1 and Hamming Loss.

22. Results (Micro-F1) • DBLP

23. Results (Macro-F1) • DBLP

24. Results (Hamming Loss) • DBLP

25. Results (Hamming Loss) • IMDb

26. Results (Hamming Loss) • YouTube

27. Conclusion • Links in multi-relational networks are heterogeneous. • SCRN exploits label homophily while simultaneously leveraging socialfeature similarity through the introduction of class propagation probabilities. • Significantly boosts classification performance on multi-labelcollaboration networks. • Our open-source implementation of SCRN is available at: http://code.google.com/p/multilabel-classification-on-social-network/

28. Reference • MACSKASSY, S. A., AND PROVOST, F. A simple relational classifier. In Proceedings of the Second Workshop on Multi-Relational Data Mining (MRDM) at KDD, 2003, pp. 64–76. • TANG, L., AND LIU, H. Relational learning via latent social dimensions. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD), 2009, pp. 817–826. • TANG, L., AND LIU, H. Scalable learning of collective behavior based on sparse social dimensions. In Proceedings of International Conference on Information and Knowledge Management (CIKM), 2009, pp. 1107-1116. • NEVILLE, J., GALLAGHER, B., ELIASSI-RAD, T., AND WANG, T. Correcting evaluation bias of relational classifiers with network cross validation. Knowledge and Information Systems (KAIS), 2011, pp. 1–25.

29. Thank you!