{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:02:49Z","timestamp":1765357369366,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583476","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"567-577","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9003-5850","authenticated-orcid":false,"given":"Susheel","family":"Suresh","sequence":"first","affiliation":[{"name":"Purdue University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-7978","authenticated-orcid":false,"given":"Mayank","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3342-7804","authenticated-orcid":false,"given":"Arko","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Microsoft, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8108-4899","authenticated-orcid":false,"given":"Jennifer","family":"Neville","sequence":"additional","affiliation":[{"name":"Microsoft Research, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8876-4280","authenticated-orcid":false,"given":"Pan","family":"Li","sequence":"additional","affiliation":[{"name":"Purdue University, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05710-7_37"},{"key":"e_1_3_2_1_2_1","volume-title":"A theorem on Fourier-Stieltjes integrals. Collected Papers of Salomon Bochner 2","author":"Bochner Salomon","year":"1992","unstructured":"Salomon Bochner. 1992. A theorem on Fourier-Stieltjes integrals. Collected Papers of Salomon Bochner 2 (1992)."},{"key":"e_1_3_2_1_3_1","first-page":"23","article-title":"From ranknet to lambdarank to lambdamart: An overview","volume":"11","author":"Burges JC","year":"2010","unstructured":"Christopher\u00a0JC Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning 11, 23-581 (2010), 81.","journal-title":"Learning"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Zhe Cao Tao Qin Tie-Yan Liu Ming-Feng Tsai and Hang Li. 2007. Learning to rank: from pairwise approach to listwise approach. In ICML. 129\u2013136.","DOI":"10.1145\/1273496.1273513"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Jianxin Chang Chen Gao Yu Zheng Yiqun Hui Yanan Niu Yang Song Depeng Jin and Yong Li. 2021. Sequential recommendation with graph neural networks. In SIGIR. 378\u2013387.","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Lun Du Yun Wang Guojie Song Zhicong Lu and Junshan Wang. 2018. Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding.. In IJCAI Vol.\u00a02018. 2086\u20132092.","DOI":"10.24963\/ijcai.2018\/288"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_11_1","volume-title":"Inductive representation learning on large graphs. NeurIPS 30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS 30 (2017)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01588-5"},{"key":"e_1_3_2_1_13_1","volume-title":"Temporal networks. Physics reports 519, 3","author":"Holme Petter","year":"2012","unstructured":"Petter Holme and Jari Saram\u00e4ki. 2012. Temporal networks. Physics reports 519, 3 (2012), 97\u2013125."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In WWW. 271\u2013279.","DOI":"10.1145\/775152.775191"},{"volume-title":"Self-attentive sequential recommendation","author":"Kang Wang-Cheng","key":"e_1_3_2_1_15_1","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. IEEE, 197\u2013206."},{"key":"e_1_3_2_1_16_1","volume-title":"Time2vec: Learning a vector representation of time. arXiv preprint arXiv:1907.05321","author":"Kazemi Seyed\u00a0Mehran","year":"2019","unstructured":"Seyed\u00a0Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, and Marcus Brubaker. 2019. Time2vec: Learning a vector representation of time. arXiv preprint arXiv:1907.05321 (2019)."},{"key":"e_1_3_2_1_17_1","first-page":"1","article-title":"Representation Learning for Dynamic Graphs: A Survey.","volume":"21","author":"Kazemi Seyed\u00a0Mehran","year":"2020","unstructured":"Seyed\u00a0Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. 2020. Representation Learning for Dynamic Graphs: A Survey.JMLR 21, 70 (2020), 1\u201373.","journal-title":"JMLR"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Srijan Kumar Xikun Zhang and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In KDD. 1269\u20131278.","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"J\u00e9r\u00f4me Kunegis. 2013. Konect: the koblenz network collection. In WWW. 1343\u20131350.","DOI":"10.1145\/2487788.2488173"},{"key":"e_1_3_2_1_20_1","volume-title":"Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. NeurIPS","author":"Li Pan","year":"2020","unstructured":"Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. 2020. Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. NeurIPS (2020)."},{"key":"e_1_3_2_1_21_1","volume-title":"The link-prediction problem for social networks. Journal of the American society for information science and technology","author":"Liben-Nowell David","year":"2007","unstructured":"David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. Journal of the American society for information science and technology (2007)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Yunyu Liu Jianzhu Ma and Pan Li. 2022. Neural Predicting Higher-order Patterns in Temporal Networks. In WWW. arxiv:2106.06039\u00a0[cs.SI]","DOI":"10.1145\/3485447.3512181"},{"key":"e_1_3_2_1_23_1","volume-title":"Paul erdos is eighty 2, 1-46","author":"Lov\u00e1sz L\u00e1szl\u00f3","year":"1993","unstructured":"L\u00e1szl\u00f3 Lov\u00e1sz. 1993. Random walks on graphs. Combinatorics, Paul erdos is eighty 2, 1-46 (1993), 4."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Ziyi Guo Zhaocun Ren Jiliang Tang and Dawei Yin. 2020. Streaming graph neural networks. In SIGIR. 719\u2013728.","DOI":"10.1145\/3397271.3401092"},{"volume-title":"dynnode2vec: Scalable dynamic network embedding","author":"Mahdavi Sedigheh","key":"e_1_3_2_1_25_1","unstructured":"Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2018. dynnode2vec: Scalable dynamic network embedding. In IEEE Big Data. IEEE, 3762\u20133765."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107000"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2133841100"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Giang\u00a0Hoang Nguyen John\u00a0Boaz Lee Ryan\u00a0A Rossi Nesreen\u00a0K Ahmed Eunyee Koh and Sungchul Kim. 2018. Continuous-time dynamic network embeddings. In WWW. 969\u2013976.","DOI":"10.1145\/3184558.3191526"},{"key":"e_1_3_2_1_29_1","volume-title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In International Conference on Learning Representations.","author":"Oono Kenta","year":"2020","unstructured":"Kenta Oono and Taiji Suzuki. 2020. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_31_1","volume-title":"Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In AAAI, Vol.\u00a034. 5363\u20135370.","author":"Pareja Aldo","year":"2020","unstructured":"Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. 2020. Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In AAAI, Vol.\u00a034. 5363\u20135370."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_33_1","unstructured":"Ali Rahimi and Benjamin Recht. 2007. Random features for large-scale kernel machines. In NeurIPS Vol.\u00a020."},{"key":"e_1_3_2_1_34_1","volume-title":"Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_1_36_1","volume-title":"Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey","author":"Skardinga Joakim","year":"2021","unstructured":"Joakim Skardinga, Bogdan Gabrys, and Katarzyna Musial. 2021. Foundations and modelling of dynamic networks using dynamic graph neural networks: A survey. IEEE Access (2021)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Weiping Song Zhiping Xiao Yifan Wang Laurent Charlin Ming Zhang and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In WSDM. 555\u2013563.","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_3_2_1_38_1","volume-title":"On the equivalence between positional node embeddings and structural graph representations. arXiv preprint arXiv:1910.00452","author":"Srinivasan Balasubramaniam","year":"2019","unstructured":"Balasubramaniam Srinivasan and Bruno Ribeiro. 2019. On the equivalence between positional node embeddings and structural graph representations. arXiv preprint arXiv:1910.00452 (2019)."},{"key":"e_1_3_2_1_39_1","volume-title":"Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. PMLR, 3462\u20133471.","author":"Trivedi Rakshit","year":"2017","unstructured":"Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. PMLR, 3462\u20133471."},{"key":"e_1_3_2_1_40_1","volume-title":"Dyrep: Learning representations over dynamic graphs. In ICLR.","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. Dyrep: Learning representations over dynamic graphs. In ICLR."},{"key":"e_1_3_2_1_41_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Andrew\u00a0Z Wang Rex Ying Pan Li Nikhil Rao Karthik Subbian and Jure Leskovec. 2021. Bipartite Dynamic Representations for Abuse Detection. In KDD. 3638\u20133648.","DOI":"10.1145\/3447548.3467141"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_1_44_1","unstructured":"Yanbang Wang Yen-Yu Chang Yunyu Liu Jure Leskovec and Pan Li. 2021. Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks. In ICLR."},{"key":"e_1_3_2_1_45_1","volume-title":"TEDIC: Neural modeling of behavioral patterns in dynamic social interaction networks. In WWW. 693\u2013705.","author":"Wang Yanbang","year":"2021","unstructured":"Yanbang Wang, Pan Li, Chongyang Bai, and Jure Leskovec. 2021. TEDIC: Neural modeling of behavioral patterns in dynamic social interaction networks. In WWW. 693\u2013705."},{"key":"e_1_3_2_1_46_1","first-page":"15915","article-title":"Self-attention with Functional Time Representation Learning","volume":"32","author":"Xu Da","year":"2019","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2019. Self-attention with Functional Time Representation Learning. NeurIPS 32 (2019), 15915\u201315925.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_47_1","unstructured":"Da Xu Chuanwei Ruan Evren Korpeoglu Sushant Kumar and Kannan Achan. 2020. Inductive representation learning on temporal graphs. In ICLR."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Dawei Yin Yuening Hu Jiliang Tang Tim Daly Mianwei Zhou Hua Ouyang Jianhui Chen Changsung Kang Hongbo Deng Chikashi Nobata 2016. Ranking relevance in yahoo search. In KDD. 323\u2013332.","DOI":"10.1145\/2939672.2939677"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Jiaxuan You Jonathan\u00a0M Gomes-Selman Rex Ying and Jure Leskovec. 2021. Identity-aware Graph Neural Networks. In AAAI Vol.\u00a035. 10737\u201310745.","DOI":"10.1609\/aaai.v35i12.17283"},{"key":"e_1_3_2_1_51_1","first-page":"5165","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. NeurIPS 31 (2018), 5165\u20135175.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_52_1","volume-title":"Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. NeurIPS 34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021. Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. NeurIPS 34 (2021)."},{"key":"e_1_3_2_1_53_1","volume-title":"Dynamic graph neural networks for sequential recommendation. arXiv preprint arXiv:2104.07368","author":"Zhang Mengqi","year":"2021","unstructured":"Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. 2021. Dynamic graph neural networks for sequential recommendation. arXiv preprint arXiv:2104.07368 (2021)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Lekui Zhou Yang Yang Xiang Ren Fei Wu and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In AAAI Vol.\u00a032.","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"e_1_3_2_1_55_1","volume-title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. arXiv preprint arXiv:2106.06935","author":"Zhu Zhaocheng","year":"2021","unstructured":"Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. 2021. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. arXiv preprint arXiv:2106.06935 (2021)."}],"event":{"name":"WWW '23: The ACM Web Conference 2023","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Austin TX USA","acronym":"WWW '23"},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583476","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:53Z","timestamp":1750178873000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":54,"alternative-id":["10.1145\/3543507.3583476","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583476","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}