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Temporal Network Representation Learning Via Historical a a 001 010 010 001 d a d 010 a 001 010 010 000101 d 010 001 010 b c b 00c1 f b 001 b 010 c 010 e c f e 010 010 e e 010 g q g q a a 111 111 d 010 a 100 100 011 f a 111 111 d 010 100 1b00 001 011 f c b c i 100 100 b 001 100 e c 100 b c 010 i i 100 100 100 h 100 010 e 100 g 010 100 1h00 i 010 h o qg 100 h o o q o 3 2 DAG T ransformation 4 a a a 111 101 110 101 111 111 110 111 b b b 100 c c 000 100 c 010 000 010 110 110 000 h 010 h q g e q e g 000 010 G_gate1 G_gate2 G_universal a b c d e,f g,h,i a 3 1 2 5 4 2 3 6 Categories Strategies Algorithms Temporal Network Representation?, PMC, Learning via EasyIM Historical Neighborhoodslow Aggregation memory requirement & results of high quality Shixun Huangy Zhifeng Baoy Guoliang Liz Yanghao? Zhouy J. Shane Culpeppery yRMIT University, Australia zTsinghua University, China Abstract—Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied 4 2015 to various real-life applications such as visualization, node clas- 3 2013 sification, and link prediction. Although significant progress has 2011 5 2016 been made on this problem in recent years, several important 4 2015 challenges remain, such as how to properly capture temporal 2013 2017 3 2011 information in evolving networks. In practice, most networks 1 2016 6 5 2016 2011 2017 are continually evolving. Some networks only add new edges or 1 6 2011 20142016 nodes such as authorship networks, while others support removal 2014 8 of nodes or edges such as internet data routing. If patterns exist 20120122 2018 8 in the changes of the network structure, we can better understand 2 2018 2017 the relationships between nodes and the evolution of the network, 7 20717 which can be further leveraged to learn node representations with more meaningful information. In this paper, we propose the Fig. 1: An example of a co-author temporal network. Each Embedding via Historical Neighborhoods Aggregation (EHNA) edge is annotated with a timestamp denoting when the edge algorithm. More specifically, we first propose a temporal random was created. walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. Then we apply a deep learning model which uses a custom attention mechanism to in- duce node embeddings that directly capture temporal information 2 3 2 3 4 4 5 6 5 6 7 8 in the underlying feature representation. We perform extensive experiments on a range of real-world datasets, and the results 2012 2013 2017 2018 demonstrate the effectiveness of our new approach in the network Fig. 2: The evolution of a set of nodes that are contextually reconstruction task and the link prediction task. and temporally related to node 1. I. INTRODUCTION Network embedding has become a valuable tool for solving a wide variety of network algorithmic problems in recent years. maximization [7], to name a few. Despite the clear value The key idea is to learn low-dimensional representations for of such information, temporal network embeddings are rarely nodes in a network. It has been applied in various applications, applied to solve many other important tasks such as network such as link prediction [1], network reconstruction [2], node reconstruction and link prediction [8, 9]. classification [3] and visualization [4]. Existing studies [1, 2, Incorporating temporal information into network embeddings 3, 4] generally learn low-dimensional representations of nodes can improve the effectiveness in capturing relationships between over a static network structure by making use of contextual nodes which can produce performance improvements in down- information such as graph proximity. stream tasks. To better understand the relationships between However, many real-world networks, such as co-author nodes with temporal information in a network, consider Figure 1 arXiv:2003.13212v1 [cs.LG] 30 Mar 2020 networks and social networks, have a wealth of temporal which is an ego co-author network for a node (1). Each node information (e.g., when edges were formed). Such temporal is an author, and edges represent collaborations denoted with information provides important insights into the dynamics of timestamps. Without temporal knowledge, the relationships networks, and can be combined with contextual information in between nodes 2, 3, 4, 6 and 7 are indistinguishable since order to learn more effective and meaningful representations they are all connected to node 1. However, when viewed from of nodes. Moreover, many application domains are heavily the temporal perspective, node 1 once was ‘close’ to nodes 2 reliant on temporal graphs, such as instant messaging networks and 3 but now is ‘closer’ to nodes 4, 6 and 7 as node 1 has and financial transaction graphs, where timing information is more recent and frequent collaborations with the latter nodes. critical. The temporal/dynamic nature of social networks has Furthermore, in the static case, nodes 2 and 3 appear to be attracted considerable research attention due to its importance in ‘closer’ to node 1 than node 5, since nodes 2 and 3 are direct many problems, including dynamic personalized pagerank [5], neighbors of node 1, whereas node 5 is not directly connected advertisement recommendation [6] and temporal influence to node 1. A temporal interpretation suggests that node 5 is also important to 1 because node 5 could be enabling collaborations 1Zhifeng Bao is the corresponding author. between nodes 1 6, and 7. Thus, with temporal information, new interpretations of the ‘closeness’ of relationships between relevant nodes from historical neighborhoods that may influence node 1 and other nodes and how these relationships evolve are new edge formations. Specifically, our temporal random walk now possible. algorithm models the relevance of nodes using a configurable To further develop this intuition, consider the following time decay, where nodes with high contextual and temporal concrete example. Suppose node 1 began publishing as a relevance are visited with higher probabilities. Our temporal Ph.D. student. Thus, papers produced during their candidature random walk algorithm can preserve the breadth-first search were co-authored with a supervisor (node 3) and one of (BFS) and depth-first search (DFS) characteristics of traditional the supervisor’s collaborators (node 2). After graduation, solutions, which can then be further exploited to provide both the student (node 1) became a research scientist in a new micro and macro level views of neighborhood structure for organization. As a result, a new collaboration with a senior the targeted application. Second, we also introduce a two-level supervisor (node 4) is formed. Node 1 was introduced to node (node level and walk level) aggregation strategy combined 5 through collaborations between the senior supervisor (node with stacked LSTMs to effectively extract features from nodes 4) and node 5, and later began working with node 6 after related to chronological events (when edges are formed). Third, the relationship with node 5 was formed. Similarly, after a we propose a temporal attention mechanism to improve the collaboration between node 1 and node 6, node 1 met node quality of the temporal feature representations being learned 7 because of the relationships between node 5 and node 8, based on both contextual and temporal relevance of the nodes. node 8 and node 7, and node 6 and node 7. Hence, as shown Our main contributions to solve the problem of temporal in Figure 2, both the formation of the relationships between network representation learning are: node 1 and other nodes and the levels of closeness of such • We leverage temporal information to analyze edge forma- relationships evolve as new connections are made between tions such that the learned embeddings can preserve both node 1 and related collaborators. structural network information (e.g., the first and second Thus, the formation of each edge (x; y) changes not only the order proximity) as well as network evolution. relationship between x and y but also relationships between • We present a temporal random walk algorithm which nodes in the surrounding neighborhood. Capturing how and why dynamically captures node relationships from historical a network evolves (through edge formations) can produce better graph neighborhoods. signals in learned models. Recent studies capture the signal • We deploy our temporal random walk algorithm in a by periodically probing a network to learn more meaningful stacked LSTM architecture that is combined with a embeddings. These methods model changes in the network by two-level temporal attention and aggregation strategy segmenting updates into fixed time windows, and then learn an developed specifically for graph data, and describe how embedding for each network snapshot [10, 11]. The embeddings to directly tune the temporal effects captured in feature of previous snapshots can be used to infer or indicate various embeddings learned by the model. patterns as the graph changes between snapshots. In particular, • We show how to effectively aggregate the resulting CTDNE [12] leveraged temporal information to sample time- temporal node features into a fixed-sized readout layer constrained random walks and trained a skip-gram model (feature embedding), which can be directly applied to such that nodes co-occurring in these random walks produce several important graph problems such as link prediction. similar embeddings. Inspired by the Hawkes process [13] which • We validate the effectiveness of our approach using four shows that the occurrence of current events are influenced by real-world datasets for the network reconstruction and link the historical events, HTNE [14] leveraged prior neighbor prediction tasks.
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