14 papers · ranked by Valyu relevance
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
Saiful Islam, Md. Nahid Hasan, Pitambar Khanra
Classification Authors: ['Saiful Islam' 'Md. Nahid Hasan' 'Pitambar Khanra'] The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders…
Jinyin Chen, Haiyang Xiong, Haibin Zheng, Dunjie Zhang + 3 more
'Mingwei Jia' 'Yi Liu'] Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial training, whereas others may trade off clean example performance, such as…
Masoud Kargar, Nasim Jelodari, Alireza Assadzadeh
graphs and graph convolutional networks for high-level feature extraction Authors: ['Masoud Kargar' 'Nasim Jelodari' 'Alireza Assadzadeh'] Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections Multiple…
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
Ali Deeb, Abdalrahman Ibrahim, Mohamed Salem, Joachim Pichler + 6 more
'Sergii Tkachov' 'Anjeza Karaj' 'Fadi Al Machot' 'Kyamakya Kyandoghere' 'Marco Lanuzza' 'Lionel Trojman'] Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli…
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz + 4 more
Networks are often categorized according to the underlying phenomena that they represent, such as re-tweets, protein interactions, or web page links. It is generally believed that networks from different categories have inherently unique network characteristics. In this work, we find strong evidence supporting this…
Jose Lugo-Martinez, Predrag Radivojac
Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
Vincent Guillemot, Arthur Tenenhaus, Laurent Le Brusquet, Vincent Frouin + 1 more
'Vincent Frouin' 'Arkady B. Khodursky'] Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest and because it helps in the interpretation of the final classifier. We present a…
Serkan Ucer, Tansel Ozyer, Reda Alhajj
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which…
Jiate Li, Binghui Wang
Perturbations with Deterministic Certification Authors: ['Jiate Li' 'Binghui Wang'] Graph neural networks (GNNs) achieve the state-of-the-art on graph-relevant tasks such as node and graph classification. However, recent works show GNNs are vulnerable to adversarial perturbations include the perturbation on edges…
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
Steven B. Gillispie, Michael D. Perlman
Graphical Markov models determined by acyclic digraphs (ADGs), also called directed acyclic graphs (DAOs), are widely studied in statistics, computer science (as Bayesian networks), operations research (as influence diagrams), and many related fields. Because different ADOs may determine the same Markov equivalence…