Search Results for author: Fragkiskos D. Malliaros

Found 17 papers, 8 papers with code

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

1 code implementation28 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.

Time-varying Signals Recovery via Graph Neural Networks

no code implementations22 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.

Graph Learning Time Series +1

Higher-order Sparse Convolutions in Graph Neural Networks

no code implementations21 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.

On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

1 code implementation5 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.

Representation Learning

Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization

1 code implementation24 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.

Topic-aware latent models for representation learning on networks

no code implementations10 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.

Community Detection Link Prediction +3

Multiple Kernel Representation Learning on Networks

1 code implementation9 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.

Link Prediction Node Classification +1

GraphSVX: Shapley Value Explanations for Graph Neural Networks

1 code implementation18 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.

NodeSig: Binary Node Embeddings via Random Walk Diffusion

no code implementations1 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.

Graph Representation Learning Link Prediction +1

Exponential Family Graph Embeddings

no code implementations20 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.

Graph Embedding Graph Learning +3

Kernel Node Embeddings

1 code implementation8 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.

Link Prediction Node Classification

Multi-task Learning for Influence Estimation and Maximization

3 code implementations18 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).

Multi-Task Learning Representation Learning

TNE: A Latent Model for Representation Learning on Networks

no code implementations16 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.

Community Detection Link Prediction +3

BiasedWalk: Biased Sampling for Representation Learning on Graphs

1 code implementation7 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.

Community Detection General Classification +3

Perturb and Combine to Identify Influential Spreaders in Real-World Networks

no code implementations13 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.

Clustering and Community Detection in Directed Networks: A Survey

no code implementations5 Aug 2013 Fragkiskos D. Malliaros, Michalis Vazirgiannis

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

Clustering Community Detection +2

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