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1 code implementation • 28 Apr 2023 • Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.

no code implementations • 22 Feb 2023 • Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros

The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.

no code implementations • 21 Feb 2023 • Jhony H. Giraldo, Sajid Javed, Arif Mahmood, Fragkiskos D. Malliaros, Thierry Bouwmans

Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.

1 code implementation • 5 Dec 2022 • Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros

Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains.

1 code implementation • 24 Jan 2022 • Bin Liu, Dimitrios Papadopoulos, Fragkiskos D. Malliaros, Grigorios Tsoumakas, Apostolos N. Papadopoulos

Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.

no code implementations • 10 Nov 2021 • Abdulkadir Çelikkanat, Fragkiskos D. Malliaros

Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering.

no code implementations • 10 Aug 2021 • George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis, Fragkiskos D. Malliaros

Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.

1 code implementation • 9 Jun 2021 • Abdulkadir Celikkanat, Yanning Shen, Fragkiskos D. Malliaros

In particular, we propose a weighted matrix factorization model that encodes random walk-based information about nodes of the network.

1 code implementation • 18 Apr 2021 • Alexandre Duval, Fragkiskos D. Malliaros

Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data due to the incorporation of graph structure into the learning of node representations, which renders their comprehension challenging.

no code implementations • 1 Oct 2020 • Abdulkadir Çelikkanat, Fragkiskos D. Malliaros, Apostolos N. Papadopoulos

Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications.

no code implementations • 20 Nov 2019 • Abdulkadir Çelikkanat, Fragkiskos D. Malliaros

We introduce the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions.

1 code implementation • 8 Sep 2019 • Abdulkadir Çelikkanat, Fragkiskos D. Malliaros

Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification.

3 code implementations • 18 Apr 2019 • George Panagopoulos, Fragkiskos D. Malliaros, Michalis Vazirgiannis

The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors).

no code implementations • 16 Oct 2018 • Abdulkadir Çelikkanat, Fragkiskos D. Malliaros

Although various approaches have been proposed to compute node embeddings, many successful methods benefit from random walks in order to transform a given network into a collection of sequences of nodes and then they target to learn the representation of nodes by predicting the context of each vertex within the sequence.

1 code implementation • 7 Sep 2018 • Duong Nguyen, Fragkiskos D. Malliaros

We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks.

no code implementations • 13 Jul 2018 • Antoine J. -P. Tixier, Maria-Evgenia G. Rossi, Fragkiskos D. Malliaros, Jesse Read, Michalis Vazirgiannis

Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure.

no code implementations • 5 Aug 2013 • Fragkiskos D. Malliaros, Michalis Vazirgiannis

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science.

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