2 code implementations • 3 Mar 2024 • Iakovos Evdaimon, Giannis Nikolentzos, Michail Chatzianastasis, Hadi Abdine, Michalis Vazirgiannis

Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.

1 code implementation • 5 Feb 2024 • Giannis Nikolentzos, Siyun Wang, Johannes Lutzeyer, Michalis Vazirgiannis

We then propose a new machine learning model for tabular data, the so-called Graph Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a nearly complete graph and which employs a synchronous message passing scheme.

no code implementations • 6 Aug 2023 • Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis

We evaluate TPCNN on both interpolation and classification tasks involving real-world irregularly sampled multivariate time series datasets.

no code implementations • 11 Jul 2023 • Michail Chatzianastasis, Giannis Nikolentzos, Michalis Vazirgiannis

Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks.

1 code implementation • 9 Jun 2023 • Gaspard Michel, Giannis Nikolentzos, Johannes Lutzeyer, Michalis Vazirgiannis

We derive three different variants of the PathNN model that aggregate single shortest paths, all shortest paths and all simple paths of length up to K. We prove that two of these variants are strictly more powerful than the 1-WL algorithm, and we experimentally validate our theoretical results.

Ranked #7 on Graph Classification on Peptides-func

no code implementations • 21 Apr 2023 • Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis

In recent years, graph neural networks (GNNs) have achieved great success in the field of graph representation learning.

no code implementations • 4 Nov 2022 • Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis

In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs.

no code implementations • 27 Jul 2022 • Chrysoula Kosma, Giannis Nikolentzos, Nancy Xu, Michalis Vazirgiannis

Recently neural network architectures have been widely applied to the problem of time series forecasting.

no code implementations • 27 May 2022 • Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström

Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images.

no code implementations • 1 Dec 2021 • Hadi Abdine, Yanzhu Guo, Moussa Kamal Eddine, Giannis Nikolentzos, Stamatis Outsios, Guokan Shang, Christos Xypolopoulos, Michalis Vazirgiannis

DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep learning.

1 code implementation • 5 Oct 2021 • Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis

In this paper, we propose a new graph neural network model, so-called $\pi$-GNN which learns a "soft" permutation (i. e., doubly stochastic) matrix for each graph, and thus projects all graphs into a common vector space.

no code implementations • 29 Sep 2021 • Giannis Nikolentzos, Michalis Vazirgiannis

The proposed model retains the transparency of Random Walk Graph Neural Networks since its first layer also consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using the geometric random walk kernel.

no code implementations • 17 Feb 2021 • George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis

Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).

no code implementations • 1 Jan 2021 • Giannis Nikolentzos, George Panagopoulos, Michalis Vazirgiannis

Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.

no code implementations • NeurIPS 2020 • Giannis Nikolentzos, Michalis Vazirgiannis

The first layer of the model consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using a random walk kernel to produce graph representations.

5 code implementations • 10 Sep 2020 • George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis

Furthermore, to account for the limited amount of training data, we capitalize on the pandemic's asynchronous outbreaks across countries and use a model-agnostic meta-learning based method to transfer knowledge from one country's model to another's.

no code implementations • 2 Mar 2020 • Changmin Wu, Giannis Nikolentzos, Michalis Vazirgiannis

Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology.

no code implementations • 1 Mar 2020 • George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis

Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes.

2 code implementations • 17 Aug 2019 • Giannis Nikolentzos, Antoine J. -P. Tixier, Michalis Vazirgiannis

In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).

Ranked #1 on Multi-Modal Document Classification on Reuters-21578

document understanding
Multi-Modal Document Classification
**+2**

1 code implementation • 13 Jul 2019 • Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis

We show that the proposed architecture can identify fundamental graph properties.

no code implementations • 27 Apr 2019 • 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.

Ranked #11 on Graph Classification on NCI1

1 code implementation • 3 Apr 2019 • Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis

In several domains, data objects can be decomposed into sets of simpler objects.

Ranked #1 on Document Classification on Twitter

1 code implementation • 7 Aug 2018 • Giannis Nikolentzos, Michalis Vazirgiannis

The first component is a kernel between vertices, while the second component is a kernel between graphs.

1 code implementation • 6 Jun 2018 • Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

no code implementations • ICLR 2018 • Giannis Nikolentzos, Polykarpos Meladianos, Antoine J-P Tixier, Konstantinos Skianis, Michalis Vazirgiannis

Graph kernels have been successfully applied to many graph classification problems.

1 code implementation • 29 Oct 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis

Graph kernels have been successfully applied to many graph classification problems.

no code implementations • EMNLP 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Fran{\c{c}}ois Rousseau, Yannis Stavrakas, Michalis Vazirgiannis

In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.

no code implementations • ICLR 2018 • Antoine Jean-Pierre Tixier, Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis

Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.

Ranked #3 on Graph Classification on RE-M12K

no code implementations • EACL 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Fran{\c{c}}ois Rousseau, Yannis Stavrakas, Michalis Vazirgiannis

Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.

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