Dual Adversarial Learning Based Network Alignment

Ren, Jiaxiang; Zhou, Yang; Jin, Ruoming; Zhang, Zijie; Dou, Dejing; Wang, Pengwei

doi:10.1109/icdm.2019.00162

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Additional machine learning-based methods for aligning graphs are introduced as search-based methods. RANA (Ren et al, 2019) uses an adversarial learning approach to embed two graphs into a space for alignment. CONE-Align (Chen et al, 2020) embeds graphs into a space to match the neighborhoods of nodes in that space.…”

Section: Graph Alignmentmentioning

confidence: 99%

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

Bhowmick

Bell

Taufer

2023

The International Journal of High Performance Computing Applica

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The convergence of extremely high levels of hardware concurrency and the effective overlap of computation and communication in asynchronous executions has resulted in increasing nondeterminism in High-Performance Computing (HPC) applications. Nondeterminism can manifest at multiple levels: from low-level communication primitives to libraries to application-level functions. No matter its source, nondeterminism can drastically increase the cost of result reproducibility, debugging workflows, testing parallel programs, or ensuring fault-tolerance. Nondeterministic executions of HPC applications can be modeled as event graphs, and the applications’ nondeterministic behavior can be understood and, in some cases, mitigated using graph comparison algorithms. However, a connection between graph comparison algorithms and approaches to understanding nondeterminism in HPC still needs to be established. This survey article moves the first steps toward establishing a connection between graph comparison algorithms and nondeterminism in HPC with its three contributions: it provides a survey of different graph comparison algorithms and a timeline for each category’s significant works; it discusses how existing graph comparison methods do not fully support properties needed to understand nondeterministic patterns in HPC applications; and it presents the open challenges that should be addressed to leverage the power of graph comparisons for the study of nondeterminism in HPC applications.

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Section: Graph Alignmentmentioning

confidence: 99%

A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

Bhowmick

Bell

Taufer

2023

The International Journal of High Performance Computing Applica

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“…MC [30] uses a matrix factorization-based network representation learning method to obtain node embedding vectors to capture the local and global structural features of nodes. IDRGM [32] estimate the distribution of perturbed graphs and maximize the distances among the perturbed nodes within the same graphs, for separating the nodes in a narrow space into a wide space. DHNA [31] designs a VGAE-based network alignment model, and two carefully thought-out restrictions are used to incorporate anchor nodes' degree differences across different networks into the encoder module.…”

Section: Related Workmentioning

confidence: 99%

Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure

Wang

Shao

et al. 2023

Algorithms

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Network alignment (NA) offers a comprehensive way to build associations between different networks by identifying shared nodes. While the majority of current NA methods rely on the topological consistency assumption, which posits that shared nodes across different networks typically have similar local structures or neighbors, we argue that anchor nodes, which play a pivotal role in NA, face a more challenging scenario that is often overlooked. In this paper, we conduct extensive statistical analysis across networks to investigate the connection status of labeled anchor node pairs and categorize them into four situations. Based on our analysis, we propose an end-to-end network alignment framework that uses node representations as a distribution rather than a point vector to better handle the structural diversity of networks. To mitigate the influence of specific nodes, we introduce a mask mechanism during the representation learning process. In addition, we utilize meta-learning to generalize the learned information on labeled anchor node pairs to other node pairs. Finally, we perform comprehensive experiments on both real-world and synthetic datasets to confirm the efficacy of our proposed method. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods significantly.

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