Meta-path-based outlier detection in heterogeneous information network
Lu LIU1,2,3,4(), Shang WANG3,5
1. Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China 2. College of Software, Jilin University, Changchun 130012, China 3. College of Computer Science and Technology, Jilin University, Changchun 130012, China 4. College of Communication Engineering, Jilin University, Changchun 130012, China 5. Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark NJ 07102, USA
Mining outliers in heterogeneous networks is crucial to many applications, but challenges abound. In this paper, we focus on identifying meta-path-based outliers in heterogeneous information network (HIN), and calculate the similarity between different types of objects. We propose a meta-path-based outlier detection method (MPOutliers) in heterogeneous information network to deal with problems in one go under a unified framework. MPOutliers calculates the heterogeneous reachable probability by combining different types of objects and their relationships. It discovers the semantic information among nodes in heterogeneous networks, instead of only considering the network structure. It also computes the closeness degree between nodes with the same type, which extends the whole heterogeneous network. Moreover, each node is assigned with a reliable weighting to measure its authority degree. Substantial experiments on two real datasets (AMiner and Movies dataset) show that our proposed method is very effective and efficient for outlier detection.
A Dalmia, M Gupta, V Varma. Query-based evolutionary graph cuboid outlier detection. In: Proceedings of the 16th International Conference on Data Mining Workshops. 2016, 85–92 https://doi.org/10.1109/ICDMW.2016.0020
3
R Kaur, S Singh. A survey of data mining and social network analysis based anomaly detection techniques. Egyptian Informatics Journal, 2016, 17(2): 199–216 https://doi.org/10.1016/j.eij.2015.11.004
4
C Shi, Y Li, J Zhang, Y Sun, P S Yu. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 17–37 https://doi.org/10.1109/TKDE.2016.2598561
5
G Pio, F Serafino, D Malerba, M Ceci. Multi-type clustering and classification from heterogeneous networks. Information Sciences, 2018, 425: 107–126 https://doi.org/10.1016/j.ins.2017.10.021
6
S Wu, S Wang. Information-theoretic outlier detection for large-scale categorical data. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 589–602 https://doi.org/10.1109/TKDE.2011.261
7
S Vijayarani, P Jothi. A hybrid clustering algorithm for outlier detection in data streams. International Journal of Grid and Distributed Computing, 2016, 9(11): 285–295 https://doi.org/10.14257/ijgdc.2016.9.11.24
8
H Dai, F Zhu, E P Lim, H Pang. Detecting anomaly collections using extreme feature ranks. Data Mining and Knowledge Discovery, 2015, 29(3): 689–731 https://doi.org/10.1007/s10618-014-0360-3
9
F Rasheed, R Alhajj. A framework for periodic outlier pattern detection in time-series sequences. IEEE Transactions on Cybernetics, 2014, 44(5): 569–582 https://doi.org/10.1109/TSMCC.2013.2261984
10
M Gupta, J Gao, C Aggarwal, J Han. Community distribution outlier detection in heterogeneous information networks. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2013, 557–573
11
M Gupta, J Gao, X Yan, H Cam, J Han. On detecting association-based clique outliers in heterogeneous information networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2013, 108–115 https://doi.org/10.1145/2492517.2492526
12
M Gupta, A Mallya, S Roy, J H D Cho, J Han. Local learning for mining outlier subgraphs from network datasets. In: Proceedings of the 2014 SIAM International Conference on Data Mining. 2014, 73–81 https://doi.org/10.1137/1.9781611973440.9
13
J Gao, F Liang, W Fan, C Wang, Y Sun, J Han. On community outliers and their efficient detection in information networks. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 813–822 https://doi.org/10.1145/1835804.1835907
14
Z Yao, P Mark, M Rabbat. Anomaly detection using proximity graph and PageRank algorithm. IEEE Transactions on Information Forensics and Security, 2012, 7(4): 1288–1300 https://doi.org/10.1109/TIFS.2012.2191963
15
M Radovanovic, A Nanopoulos, M Ivanovic. Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(5): 1369–1382 https://doi.org/10.1109/TKDE.2014.2365790
M Gupta, J Gao, C C Aggarwal, J Han. Outlier detection for temporal data: a survey. IEEE Transactions on Data and Engineering, 2014, 26(9): 2250–2267 https://doi.org/10.1109/TKDE.2013.184
18
J Zhang, H Li, Q Gao, H Wang, Y Luo. Detecting anomalies from big network traffic data using an adaptive detection approach. Information Sciences, 2015, 318: 91–110 https://doi.org/10.1016/j.ins.2014.07.044
19
C C Aggarwal, Y Zhao, P S Yu. Outlier detection in graph streams. In: Proceedings of International Conference on Data Engineering. 2011, 399–409 https://doi.org/10.1109/ICDE.2011.5767885
20
L Akoglu, H Tong, D Koutra. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 2015, 29(3): 626–688 https://doi.org/10.1007/s10618-014-0365-y
21
S N Yin, H S Kang, S R Kim. Clustering algorithm based on outlier detection for anomaly intrusion detection. Journal of Internet Technology, 2016, 17(2): 291–299
22
M Gupta, J Gao, Y Sun, J Han. Integrating community matching and outlier detection for mining evolutionary community outliers. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 859–867 https://doi.org/10.1145/2339530.2339667
23
H Zhuang, J Zhang, G Brova, J Tang, H Cam, X Yan, J Han. Mining query-based subnetwork outliers in heterogeneous information networks. In: Proceedings of IEEE International Conference on DataMining. 2014, 1127–1132 https://doi.org/10.1109/ICDM.2014.85
24
J Kuck, H Zhuang, X Yan, H Cam, J Han. Query-based outlier detection in heterogeneous information networks. In: Proceedings of the 18th International Conference on Extending Database Technology. 2015, 325–336
25
S Kim, N W Cho, Y J Lee, S H Kang, T Kim. Application of densitybased outlier detection to database activity monitoring. Information Systems Frontiers, 2013, 15(1): 55–65 https://doi.org/10.1007/s10796-010-9266-9
26
S Liu, L Chen, L M Ni. Anomaly detection from incomplete data. ACM Transactions on Knowledge Discovery from Data, 2014, 9(2): 11 https://doi.org/10.1145/2629668
27
A Rahmani, S Afra, O Zarour. Graph-based approach for outlier detection in sequential data and its application on stock market and weather data. Knowledge-based Systems, 2014, 61: 89–97 https://doi.org/10.1016/j.knosys.2014.02.008
28
X Cao, Y Zheng, C Shi, J Li, B Wu. Link prediction in schema-rich heterogeneous information network. In: Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2016, 449–460 https://doi.org/10.1007/978-3-319-31753-3_36
29
C Shi, X Kong, Y Huang, P S Yu. HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(10): 2479–2492 https://doi.org/10.1109/TKDE.2013.2297920
30
C Shi, J Liu, F Zhuang, P S Yu, B Wu. Integrating heterogeneous information via flexible regularization framework for recommendation. Knowledge and Information Systems, 2016, 49(3): 835–859 https://doi.org/10.1007/s10115-016-0925-0
31
I Gunes, S Gunduz-Oguducu, Z Cataltepe. Link prediction using time series of neighborhood-based node similarity scores. Data Mining and Knowledge Discovery, 2016, 30(1): 147–180 https://doi.org/10.1007/s10618-015-0407-0
32
Y Sun, J Han, X Yan, P S Yu, T Wu. PathSim: meta path-based top-k similarity search in heterogeneous information networks. In: Proceedings of International Conference on Very Large Databases. 2011, 992–1003
33
J Tang, J Zhang, L Yao, J Li, L Zhang, Z Su. ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 990–998 https://doi.org/10.1145/1401890.1402008
