Attributed Network Embedding for Learning in a Dynamic Environment

Li, Jundong; Dani, Harsh; Hu, Xia; Tang, Jiliang; Chang, Yi; Liu, Huan

doi:10.1145/3132847.3132919

Cited by 332 publications

(191 citation statements)

References 49 publications

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“…Note that we do not plot the performance of NMF as its F1 scores are much lower than other models. Some work includes node attributes as additional information source when using NMF related models for node classification [11,17], but this is beyond our experiment setting. From the figures, some observations are summarized as below:…”

Section: Node Classificationmentioning

confidence: 99%

Is a Single Vector Enough?

Liu¹,

Tan²,

Li³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction. Existing network embedding models comprehensively integrate all information of each node, such as links and attributes, towards a single embedding vector to represent the node's general role in the network. However, a real-world entity could be multifaceted, where it connects to different neighborhoods due to different motives or self-characteristics that are not necessarily correlated. For example, in a movie recommender system, a user may love comedies or horror movies simultaneously, but it is not likely that these two types of movies are mutually close in the embedding space, nor the user embedding vector could be sufficiently close to them at the same time. In this paper, we propose a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling. Each facet of a node is mapped as an embedding vector, while we also maintain association degree between each pair of node and facet. The proposed method is adaptive to various existing embedding models, without significantly complicating the optimization process. We also discuss how to engage embedding vectors of different facets for inference tasks including classification and link prediction. Experiments on real-world datasets help comprehensively evaluate the performance of the proposed method.

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Section: Node Classificationmentioning

confidence: 99%

Is a Single Vector Enough?

Liu¹,

Tan²,

Li³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

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show abstract

“…In [33], the authors evaluated the effectiveness of embedding representations of DeepWalk and LINE on network clustering. Both approaches showed nearly the same performance.…”

Section: Techniques For Inspecting Embeddingsmentioning

confidence: 99%

Properties of Vector Embeddings in Social Networks

Rizi

2017

Algorithms

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Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods for inspecting embeddings usually rely on visualization methods, which do not work on a larger scale and do not give concrete interpretations of vector embeddings in terms of preserved network properties (e.g., centrality or betweenness measures). In this paper, we study and investigate network properties preserved by recent random walk-based embedding procedures like node2vec, DeepWalk or LINE. We propose a method that applies learning to rank in order to relate embeddings to network centralities. We evaluate our approach with extensive experiments on real-world and artificial social networks. Experiments show that each embedding method learns different network properties. In addition, we show that our graph embeddings in combination with neural networks provide a computationally efficient way to approximate the Closeness Centrality measure in social networks.

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“…However, all the aforementioned methods only focus on static network embedding. There are some attempts in temporal network embedding, which can be broadly classified into two categories: embedding snapshot networks [6,8,14,34,35] and modeling temporal evolution [19,26,37]. The basic idea of the former is to learn node embedding for each network snapshot.…”

Section: Related Workmentioning

confidence: 99%

“…The basic idea of the former is to learn node embedding for each network snapshot. Specifically, DANE [14] and DHPE [35] present efficient algorithms based on perturbation theory. Song et al extend skip-gram based models and propose a dynamic network embedding framework [6].…”

Section: Related Workmentioning

confidence: 99%

Temporal Network Embedding with Micro- and Macro-dynamics

Wang

Shi

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

107

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Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The microdynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale. Both micro-and macro-dynamics are the key factors to network evolution; however, how to elegantly capture both of them for temporal network embedding, especially macro-dynamics, has not yet been well studied. In this paper, we propose a novel temporal network embedding method with micro-and macro-dynamics, named M 2 DNE. Specifically, for micro-dynamics, we regard the establishments of edges as the occurrences of chronological events and propose a temporal attention point process to capture the formation process of network structures in a fine-grained manner. For macro-dynamics, we define a general dynamics equation parameterized with network embeddings to capture the inherent evolution pattern and impose constraints in a higher structural level on network embeddings. Mutual evolutions of micro-and macro-dynamics in a temporal network alternately affect the process of learning node embeddings. Extensive experiments on three real-world temporal networks demonstrate that M 2 DNE significantly outperforms the state-of-the-arts not only in traditional tasks, e.g., network reconstruction, but also in temporal tendency-related tasks, e.g., scale prediction. Definition 3.3. Macro-dynamics. Given a temporal network G = (V, E, T ), macro-dynamics refer to the evolution process of the network scale, denoted as A = {e(t)} | T | t =t 1 , where e(t) is the cumulative number of edges by time t.

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Attributed Network Embedding for Learning in a Dynamic Environment

Cited by 332 publications

References 49 publications

Is a Single Vector Enough?

Is a Single Vector Enough?

Properties of Vector Embeddings in Social Networks

Temporal Network Embedding with Micro- and Macro-dynamics

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