Structural representation learning for network alignment with self-supervised anchor links

Nguyen, Thanh; Pham, Minh Quang; Nguyên, Thành Tâm; Huynh, Thanh Trung; Tong, Van; Nguyen, Quoc Viet Hung; Tho, Quan Thanh

doi:10.1016/j.eswa.2020.113857

Cited by 27 publications

(14 citation statements)

References 52 publications

(67 reference statements)

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“…Graph embedding is an emerging technique in the field of graph analysis due to the ubiquity of large-scale networks in real-world applications [27]- [32]. Graph embedding techniques can be categorised into three main categories [33]- [36]: matrix factorisation, random walk, and deep learning.…”

Section: A Graph Embeddingmentioning

confidence: 99%

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Huynh

Tong

Nguyên

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user colocation and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks by an average margin of 45.7% and 29.46%, respectively. We also demonstrate the robustness of our model against adversarial conditions such as structural noise, attribute noise, and hyperparameter sensitivity.

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Section: A Graph Embeddingmentioning

confidence: 99%

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Huynh

Tong

Nguyên

et al. 2022

IEEE Trans. Knowl. Data Eng.

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“…However, understanding the structure of rumours turned out to be more important than understanding the detection itself [17,18,19,20]. Due to the graph-based propagation structures of rumours, explanations solely based on features insufficient [21,22,23,24].…”

Section: Related Workmentioning

confidence: 99%

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

Nguyên¹,

Phan²,

Nguyen³

et al. 2022

Preprint

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The propagation of rumours on social media poses an important threat to societies, so that various techniques for rumour detection have been proposed recently. Yet, existing work focuses on what entities constitute a rumour, but provides little support to understand why the entities have been classified as such. This prevents an effective evaluation of the detected rumours as well as the design of countermeasures. In this work, we argue that explanations for detected rumours may be given in terms of examples of related rumours detected in the past. A diverse set of similar rumours helps users to generalize, i.e., to understand the properties that govern the detection of rumours. Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the k most similar and diverse subgraphs from past rumours. The challenge is that all of the computations require fast assessment of similarities between graphs. To achieve an efficient and adaptive realization of the approach in a streaming setting, we present a novel graph representation learning technique and report on implementation considerations. Our evaluation experiments show that our approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.

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“…Network alignment (NA) is the task of identifying nodes belonging to the same identity across different networks [18].…”

Section: Related Workmentioning

confidence: 99%

Network Alignment with Holistic Embeddings

Huynh

Duong

Nguyen

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we propose a novel end-to-end alignment framework that can leverage different modalities to compare and align network nodes in an efficient way. In order to exploit the richness of the network context, our model constructs multiple embeddings for each node, each of which captures one modality or type of network information. We then design a late-fusion mechanism to combine the learned embeddings based on the importance of the underlying information. Our fusion mechanism allows our model to be adapted to various types of structure of the input network. Experimental results show that our technique outperforms state-of-the-art approaches in terms of accuracy on real and synthetic datasets, while being robust against various noise factors.

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Structural representation learning for network alignment with self-supervised anchor links

Cited by 27 publications

References 52 publications

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

Network Alignment with Holistic Embeddings

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