Search Results for author: Giannis Nikolentzos

Found 22 papers, 8 papers with code

Time Series Forecasting Models Copy the Past: How to Mitigate

no code implementations27 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.

Time Series Forecasting

Image Keypoint Matching using Graph Neural Networks

no code implementations27 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.

Graph Matching

NLP Research and Resources at DaSciM, Ecole Polytechnique

no code implementations1 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.

Permute Me Softly: Learning Soft Permutations for Graph Representations

1 code implementation5 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.

Graph Classification Graph Regression

Geometric Random Walk Graph Neural Networks via Implicit Layers

no code implementations29 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.

Graph Classification

Ego-based Entropy Measures for Structural Representations on Graphs

no code implementations17 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).

Graph Classification

An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks

no code implementations1 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.

Graph Similarity

Random Walk Graph Neural Networks

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.

Graph Classification

Transfer Graph Neural Networks for Pandemic Forecasting

4 code implementations10 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.

Meta-Learning Representation Learning +1

EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

no code implementations2 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.

Graph Mining

Ego-based Entropy Measures for Structural Representations

no code implementations1 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.

General Classification Graph Classification

Message Passing Attention Networks for Document Understanding

2 code implementations17 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).

text-classification Text Classification

Graph Kernels: A Survey

no code implementations27 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.

Graph Classification

Message Passing Graph Kernels

1 code implementation7 Aug 2018 Giannis Nikolentzos, Michalis Vazirgiannis

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

Graph Similarity

GraKeL: A Graph Kernel Library in Python

1 code implementation6 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.

General Classification Graph Classification

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