Multi-label Relational Neighbor Classification
using Social Context Features
Xi Wang and Gita Sukthankar
Department of EEC...
Motivation
 The conventional relational
classification model focuses on
the single-label classification
problem.
 Real-w...
Problem Formulation
 Node classification in multi-relational networks
 Input:
 Network structure (i.e., connectivity in...
Classification in Networked Data
 Homophily: nodes with similar labels are more likely to be
connected
 Markov assumptio...
Contribution
 A new multi-label iterative relational neighbor classifier
(SCRN)
 Extract social context features using e...
Relational Neighbor Classifier
 The Relational Neighbor (RN) classifier proposed by Macskassy et al.
(MRDM’03), is a simp...
Relational Neighbor Classifier
 Weighted-vote relational neighbor classifier (wvRN)
estimates prediction probability as:
...
Apply RN in Multi-relational Network
Ground truth
: nodes with both labels (red, green)
: nodes with green label only
: no...
Edge-Based Social Feature Extraction
 Connections in human networks are mainly affiliation-
driven.
 Since each connecti...
Cluster edges using K-Means
 Scalable edge clustering method proposed by Tang et al.
(CIKM’09).
 Each edge is represente...
Edge-Clustering Visualization
Figure: A subset of DBLP with 95 instances. Edges are clustered into 10
groups, with each sh...
Proposed Method: SCRN
 The initial set of reference features for class c can be
defined as the weighted sum of social fea...
SCRN
 SCRN estimates the class-membership probability of node
belonging to class c using the following equation:
P(li
c
|...
SCRN Overview
Input: , Max_Iter
Output: for nodes in
1. Construct nodes’ social feature space
2. Initialize the class refe...
SCRN Visualization
Figure: SCRN on synthetic multi-label network with 1000 nodes and 32 classes
(15 iterations).
14
Datasets
DBLP
 We construct a weighted collaboration network for
authors who have published at least 2 papers during the...
Datasets
IMDb
 We extract movies and TV shows released between
2000 and 2010, and those directed by the same director
ar...
Datasets
YouTube
 A subset of data (15000 nodes) from the original
YouTube dataset[1] using snowball sampling.
 Each us...
Comparative Methods
Edge (EdgeCluster)
wvRN
Prior
Random
18
Experiment Setting
 Size of social feature space :
 1000 for DBLP and YouTube; 10000 for IMDb
 Class propagation probab...
Experiment Setting
 We employ the network cross-validation (NCV) method
(KAIS’11) to reduce the overlap between test samp...
Results (Micro-F1)
DBLP
10
20
30
40
50
60
70
5 10 15 20 25 30
Micro-F1accuracy(%)
Training data percentage(%)
SCRN
Edge
w...
Results (Macro-F1)
DBLP
10
20
30
40
50
60
70
5 10 15 20 25 30
Macro-F1accuracy(%)
Training data percentage (%)
SCRN
Edge
...
Results (Hamming Loss)
DBLP
23
Results (Hamming Loss)
IMDb
24
Results (Hamming Loss)
YouTube
25
Conclusion
 Links in multi-relational networks are heterogeneous.
 SCRN exploits label homophily while simultaneously
le...
Reference
 MACSKASSY, S. A., AND PROVOST, F. A simple relational classifier. In
Proceedings of the Second Workshop on Mul...
Thank you!
28
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2013 KDD conference presentation--"Multi-Label Relational Neighbor Classification using Social Context Features"

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  • This class-propagation probability captures the node’s intrinsic likelihood of belonging to each class, and serves as a prior weight for each class when aggregating the neighbors’ class labels in the collective inference procedure.

    Our classifier extends Relational neighbor classifier by introducing a node class-propagation probability that modulates the amount of propagation that occurs in a class specific way based on the node’s similarity to each class.
  • which produces fair comparisons between different within-network classification approaches
  • The results are averaged over 10-cross validation folds
  • 2013 KDD conference presentation--"Multi-Label Relational Neighbor Classification using Social Context Features"

    1. 1. Multi-label Relational Neighbor Classification using Social Context Features Xi Wang and Gita Sukthankar Department of EECS University of Central Florida
    2. 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. Example: Scientific collaboration network Machine Learning Data Mining Artificial Intelligence 1
    3. 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 2
    4. 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. 3
    5. 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 over benchmarks on several real-world collaborative networked datasets 4
    6. 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. Iteration 1 Iteration 2Training Graph 5
    7. 7. Relational Neighbor Classifier  Weighted-vote relational neighbor classifier (wvRN) estimates prediction probability as: Here is the usual normalization factor, and is the weight of the link between node and   ij Nv jjjiii NcLPvvw z vcLP )|(),( 1 )|( z w(vi,vj ) vi vj 6
    8. 8. Apply RN in Multi-relational Network Ground truth : nodes with both labels (red, green) : nodes with green label only : nodes with red label only 7
    9. 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). 8
    10. 10. Cluster edges using K-Means  Scalable edge clustering method proposed by Tang et al. (CIKM’09).  Each edge is represented in 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. Original network Step1 : Edge representations Step2: Construct social features 9
    11. 11. Edge-Clustering Visualization Figure: A subset of DBLP with 95 instances. Edges are clustered into 10 groups, with each shown in a different color. 10
    12. 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: RV(c) = 1 |Vc K | P(li c =1)´SF(vi ) viÎVc K å vi PCP (li c | SF(vi ))= sim(SF(vi ), RV(c)) 11
    13. 13. SCRN  SCRN estimates the class-membership probability of node belonging to class c using the following equation: P(li c | Ni,SF(vi )) = 1 z PCP (li c | SF(vi ))´w(vi,vj )´ P(lj c | Nj ) vj ÎNi å class propagation probability similarity between connected nodes (link weight) class probability of its neighbors vi 12
    14. 14. SCRN Overview Input: , Max_Iter Output: for nodes in 1. Construct nodes’ social feature space 2. Initialize the class reference vectors for each class 3. Calculate the class-propagation probability for each test node 4. 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 {G,V,E,C,LK } LU VU 13
    15. 15. SCRN Visualization Figure: SCRN on synthetic multi-label network with 1000 nodes and 32 classes (15 iterations). 14
    16. 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: DataBase: ICDE,VLDB, PODS, EDBT Data Mining: KDD, ICDM, SDM, PAKDD Artificial Intelligence: IJCAI, AAAI Information Retrieval: SIGIR, ECIR Computer Vision: CVPR Machine Learning: ICML, ECML 15
    17. 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”. 16
    18. 18. Datasets YouTube  A subset of data (15000 nodes) from the original YouTube dataset[1] using snowball sampling.  Each user in YouTube can subscribe to different interest groups and add other users as his/her contacts.  Class labels are 47 interest groups. [1] http://www.public.asu.edu/~ltang9/social_ dimension.html 17
    19. 19. Comparative Methods Edge (EdgeCluster) wvRN Prior Random 18
    20. 20. Experiment Setting  Size of social feature space :  1000 for DBLP and YouTube; 10000 for IMDb  Class propagation probability is calculated with the Generalized Histogram Intersection Kernel.  Relaxation Labeling is used in the collective inference framework for SCRN and wvRN.  We assume the number of labels for testing nodes is known. 19
    21. 21. Experiment Setting  We employ the network cross-validation (NCV) method (KAIS’11) to reduce the overlap between test samples.  Classification performance is evaluated based on Micro-F1, Macro-F1 and Hamming Loss. 20
    22. 22. Results (Micro-F1) DBLP 10 20 30 40 50 60 70 5 10 15 20 25 30 Micro-F1accuracy(%) Training data percentage(%) SCRN Edge wvRN Prior Random 21
    23. 23. Results (Macro-F1) DBLP 10 20 30 40 50 60 70 5 10 15 20 25 30 Macro-F1accuracy(%) Training data percentage (%) SCRN Edge wvRN Prior Random 22
    24. 24. Results (Hamming Loss) DBLP 23
    25. 25. Results (Hamming Loss) IMDb 24
    26. 26. Results (Hamming Loss) YouTube 25
    27. 27. Conclusion  Links in multi-relational networks are heterogeneous.  SCRN exploits label homophily while simultaneously leveraging social feature similarity through the introduction of class propagation probabilities.  Significantly boosts classification performance on multi- label collaboration networks.  Our open-source implementation of SCRN is available at: http://code.google.com/p/multilabel-classification-on-social-network/ 26
    28. 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. 27
    29. 29. Thank you! 28

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