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Network Alignment With Holistic Embeddings

Publications associées (33)

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Privacy-Preserving Machine Learning on Graphs

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Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

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Maintenance scheduling of manufacturing systems based on optimal price of the network

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Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...

2022

Network Alignment with Holistic Embeddings (Extended Abstract)

Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information ...

IEEE COMPUTER SOC2022

MPGM: Scalable and Accurate Multiple Network Alignment

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Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...

IEEE COMPUTER SOC2020

The Sketching Complexity of Graph and Hypergraph Counting

Mikhail Kapralov, John Michael Goddard Kallaugher

Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been ...

IEEE COMPUTER SOC2018

Discovering Interaction Patterns in Online Learning Environments

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The increasing amount of data collected in online learning environments provides unique opportunities to better understand the learning processes in different educational settings. Learning analytics research aims at understanding and optimizing learning a ...

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Scalable analysis of linear networked systems via chordal decomposition

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This paper introduces a chordal decomposition approach for scalable analysis of linear networked systems, including stability, H 2 and H ∞ performance. Our main strategy is to exploit any sparsity within these analysis problems and use chordal decompositio ...

IEEE2018

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Pierre Dillenbourg, Mina Shirvani Boroujeni

In this integrated study of dynamics in MOOCs discussion forums, we analyze the interplay of temporal patterns, discussion content, and the social structure emerging from the communication using mixed methods. A special focus is on the yet under-explored a ...

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Social network analysis and qualitative interviews for assessing geographic characteristics of tourism business networks

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This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region ...

Public Library of Science2016

Network Alignment: Theory, Algorithms, and Applications

Ehsan Kazemi

Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active a ...

EPFL2016