599 citations · 1.2k across the 114 of their papers we have counts for
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An Ensemble Method for Producing Word Representations focusing on the Greek Language
Michalis Lioudakis, Stamatis Outsios, Michalis Vazirgiannis
In this paper we present a new ensemble method, Continuous Bag-of-Skip-grams (CBOS), that produces high-quality word representations putting emphasis on the modern Greek language.…
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link predic…
Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
Jean-Baptiste Remy, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis
The Hierarchical Attention Network (HAN) has made great strides, but it suffers a major limitation: at level 1, each sentence is encoded in complete isolation. In this work, we pro…
Revisiting the Graph Isomorphism Problem with Semidefinite Programming
Giannis Nikolentzos, Michalis Vazirgiannis
It is well-known that the graph isomorphism problem can be posed as an equivalent problem of determining whether an auxiliary graph structure contains a clique of specific order. H…
Message Passing Attention Networks for Document Understanding
Giannis Nikolentzos, Antoine J. -P. Tixier, Michalis Vazirgiannis
Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many task…
k-hop Graph Neural Networks
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisf…
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Guillaume Salha, Stratis Limnios, Romain Hennequin +2
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackl…
Randomised Bayesian Least-Squares Policy Iteration
Nikolaos Tziortziotis, Christos Dimitrakakis, Michalis Vazirgiannis
We introduce Bayesian least-squares policy iteration (BLSPI), an off-policy, model-free, policy iteration algorithm that uses the Bayesian least-squares temporal-difference (BLSTD)…
Graph Kernels: A Survey
Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years,…
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Guokan Shang, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis +1
Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly sum…
Multi-task Learning for Influence Estimation and Maximization
George Panagopoulos, Fragkiskos D. Malliaros, Michalis Vazirgiannis
We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabi…
Evaluation of Greek Word Embeddings
Stamatis Outsios, Christos Karatsalos, Konstantinos Skianis +1
Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance. Most research efforts are focusing on English word em…
Rep the Set: Neural Networks for Learning Set Representations
Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios +1
In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventiona…
A Degeneracy Framework for Scalable Graph Autoencoders
Guillaume Salha, Romain Hennequin, Viet Anh Tran +1
In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train…