node2vec: Scalable Feature Learning for Networks.
node2vec: Scalable Feature Learning for Networks.
复制标题
DOI：
10.1145/2939672.2939754
复制
发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Leskovec J
中科院分区：
文献类型：
--
作者：
Grover A;Leskovec J
在网络中的节点和边上的预测任务需要在学习算法所使用的工程特征中进行仔细的努力。最近在更广泛的表征学习领域的研究已经在通过学习特征本身来自动预测方面取得了重大进展。然而，目前的特征学习方法没有足够的表达能力来捕捉网络中观察到的连接模式的多样性。在这里，我们提出了node2vec，一个算法框架，用于学习网络中节点的连续特征表示。在node2vec中，我们学习节点到低维特征空间的映射，该映射最大化保留节点的网络邻域的可能性。我们定义了一个灵活的概念，一个节点的网络邻域，并设计了一个有偏的随机游走过程，有效地探索不同的社区。我们的算法概括了以前的工作，这是基于刚性的概念网络的邻居，我们认为，增加的灵活性，探索社区是学习更丰富的表示的关键。我们证明了node2vec在多标签分类和链接预测的几个现实世界的网络从不同的领域现有的国家的最先进的技术的功效。总之，我们的工作代表了一种新的方法，可以有效地学习复杂网络中最先进的任务无关表示。
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
登录
查看更多内容
影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
影响因子：
14.9
作者：
Breitkreutz BJ;Stark C;Reguly T;Boucher L;Breitkreutz A;Livstone M;Oughtred R;Lackner DH;Bähler J;Wood V;Dolinski K;Tyers M
通讯作者：
Tyers M
影响因子：
56.9
作者：
Roweis, ST;Saul, LK
通讯作者：
Saul, LK
DOI：
10.1002/asi.20591
发表时间：
2007-05-01
影响因子：
--
作者：
Liben-Nowell, David;Kleinberg, Jon
通讯作者：
Kleinberg, Jon
影响因子：
48
作者：
Radivojac, Predrag;Clark, Wyatt T.;Oron, Tal Ronnen;Schnoes, Alexandra M.;Wittkop, Tobias;Sokolov, Artem;Graim, Kiley;Funk, Christopher;Verspoor, Karin;Ben-Hur, Asa;Pandey, Gaurav;Yunes, Jeffrey M.;Talwalkar, Ameet S.;Repo, Susanna;Souza, Michael L.;Piovesan, Damiano;Casadio, Rita;Wang, Zheng;Cheng, Jianlin;Fang, Hai;Goughl, Julian;Koskinen, Patrik;Toronen, Petri;Nokso-Koivisto, Jussi;Holm, Liisa;Cozzetto, Domenico;Buchan, Daniel W. A.;Bryson, Kevin;Jones, David T.;Limaye, Bhakti;Inamdar, Harshal;Datta, Avik;Manjari, Sunitha K.;Joshi, Rajendra;Chitale, Meghana;Kihara, Daisuke;Lisewski, Andreas M.;Erdin, Serkan;Venner, Eric;Lichtarge, Olivier;Rentzsch, Robert;Yang, Haixuan;Romero, Alfonso E.;Bhat, Prajwal;Paccanaro, Alberto;Hamp, Tobias;Kassner, Rebecca;Seemayer, Stefan;Vicedo, Esmeralda;Schaefer, Christian;Achten, Dominik;Auer, Florian;Boehm, Ariane;Braun, Tatjana;Hecht, Maximilian;Heron, Mark;Hoenigschmid, Peter;Hopf, Thomas A.;Kaufmann, Stefanie;Kiening, Michael;Krompass, Denis;Landerer, Cedric;Mahlich, Yannick;Roos, Manfred;Bjorne, Jari;Salakoski, Tapio;Wong, Andrew;Shatkay, Hagit;Gatzmann, Fanny;Sommer, Ingolf;Wass, Mark N.;Sternberg, Michael J. E.;Skunca, Nives;Supek, Fran;Bosnjak, Matko;Panov, Pance;Dzeroski, Saso;Smuc, Tomislav;Kourmpetis, Yiannis A. I.;van Dijk, Aalt D. J.;ter Braak, Cajo J. F.;Zhou, Yuanpeng;Gong, Qingtian;Dong, Xinran;Tian, Weidong;Falda, Marco;Fontana, Paolo;Lavezzo, Enrico;Di Camillo, Barbara;Toppo, Stefano;Lan, Liang;Djuric, Nemanja;Guo, Yuhong;Vucetic, Slobodan;Bairoch, Amos;Linial, Michal;Babbitt, Patricia C.;Brenner, Steven E.;Orengo, Christine;Rost, Burkhard;Mooney, Sean D.;Friedberg, Iddo
通讯作者：
Friedberg, Iddo