{"type":"ScholarlyArticle","title":"Feature Hashing for Network Representation Learning","authors":[{"name":"Qixiang Wang","slug":"qixiang-wang","given":"Qixiang","family":"Wang","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding , Xidian University , Xi'an 710071 , China"},{"name":"Shanfeng Wang","slug":"shanfeng-wang","given":"Shanfeng","family":"Wang","affiliation":"School of Cyber Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Maoguo Gong","slug":"maoguo-gong","given":"Maoguo","family":"Gong","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding , Xidian University , Xi'an 710071 , China"},{"name":"Yue Wu","slug":"yue-wu","given":"Yue","family":"Wu","affiliation":"School of Computer Science and Technology , Xidian University , Xi'an 710071 , China"}],"abstract":"The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.","doi":"10.24963/ijcai.2018/390","url":"https://ijcai.org/proceedings/2018/390","pdf":"https://ijcai.org/proceedings/2018/0390.pdf","markdown":"https://ijcai.org/proceedings/2018/390.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/390","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":390,"pages":{"first":2812,"last":2818},"keywords":["Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Social Sciences","Machine Learning: Deep Learning","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2013] Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexan- der J Smola. Distributed large-scale natural graph factorization. In Proceedings of the 22nd international conference on World Wide Web, pages 37-48. ACM, 2013.","title":"Distributed large-scale natural graph factorization","authors":"Ahmed","year":"2013","venue":"Proceedings of the 22nd international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Argerich et al., 2016] Luis Argerich, Joaquín Torré Zaffa- roni, and Matías J Cano. Hash2vec, feature hashing for word embeddings. arXiv preprint arXiv:1608.08940, 2016.","title":"Hash2vec, feature hashing for word embeddings","authors":"Luis Argerich, Joaquín Torré Zaffaroni, Matías J Cano","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Attenberg et al., 2009] Josh Attenberg, Kilian Weinberger, Anirban Dasgupta, Alex Smola, and Martin Zinkevich. Collaborative email-spam filtering with the hashing trick. In Proceedings of the Sixth Conference on Email and Anti- Spam, 2009.","title":"Collaborative email-spam filtering with the hashing trick","authors":"Josh Attenberg, Kilian Weinberger, Anirban Dasgupta, Alex Smola, Martin Zinkevich","year":"2009","venue":"Proceedings of the Sixth Conference on Email and Anti-Spam","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Hamilton et al., 2017] William L Hamilton, Rex Ying, and Jure Leskovec. Representation learning on graphs: Meth- ods and applications. arXiv preprint arXiv:1709.05584, 2017.","title":"Representation learning on graphs: Methods and applications","authors":"Rex William L Hamilton, Jure Ying, Leskovec","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Liben-Nowell and Kleinberg, 2007] David Liben-Nowell and Jon Kleinberg. The link-prediction problem for social networks. journal of the Association for Information Science and Technology, 58(7):1019-1031, 2007.","title":"The link-prediction problem for social networks","authors":"Liben-Nowell, Kleinberg, David Liben-Nowell, Jon Kleinberg","year":"2007","venue":"journal of the Association for Information Science and Technology","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[McCallum et al., 2000] Andrew Kachites McCallum, Ka- mal Nigam, Jason Rennie, and Kristie Seymore. Automat- ing the construction of internet portals with machine learn- ing. Information Retrieval, 3(2):127-163, 2000.","title":"Automating the construction of internet portals with machine learning","authors":"Andrew Kachites Mccallum, Kamal Nigam, Jason Rennie, Kristie Seymore","year":"2000","venue":"Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Cor- rado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arX- iv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ou et al., 2016] Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. Asymmetric transitivity preserv- ing graph embedding. In KDD, pages 1105-1114, 2016.","title":"Asymmetric transitivity preserving graph embedding","authors":"Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and da- ta mining, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Sen et al., 2008] Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 29(3):93, 2008.","title":"Collective classification in network data","authors":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, Tina Eliassi-Rad","year":"2008","venue":"AI magazine","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Shi et al., 2009] Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, and Vishy Vishwanathan. Hash kernels. In Artificial intelligence and statistics, pages 496-503, 2009.","title":"Hash kernels","authors":"Shi","year":"2009","venue":"Artificial intelligence and statistics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pages 1067-1077. International World Wide Web Confer- ences Steering Committee, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Tsoumakas and Katakis, 2006] Grigorios Tsoumakas and Ioannis Katakis. Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 2006.","title":"Grigorios Tsoumakas and Ioannis Katakis. Multi-label classification: An overview","authors":"Katakis Tsoumakas","year":"2006","venue":"International Journal of Data Warehousing and Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Weinberger et al., 2009] Kilian Weinberger, Anirban Das- gupta, John Langford, Alex Smola, and Josh Attenberg. Feature hashing for large scale multitask learning. In Pro- ceedings of the 26th Annual International Conference on Machine Learning, pages 1113-1120. ACM, 2009.","title":"Feature hashing for large scale multitask learning","authors":"Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, Josh Attenberg","year":"2009","venue":"Proceedings of the 26th Annual International Conference on Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}