Related papers: Proceedings Tenth International Workshop on Graph …
These are the revised accepted papers from the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018), Barcelona, Spain, September 26 - September 28, 2018. Proceedings are also to be published by Springer in the…
Graph-related applications have experienced significant growth in academia and industry, driven by the powerful representation capabilities of graph. However, efficiently executing these applications faces various challenges, such as load…
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility…
This volume contains the post-proceedings of the 8th International Workshop on Computing with Terms and Graphs (TERMGRAPH 2014). The workshop took place in Vienna on July 13, 2014 and was affiliated with the joint RTA and TLCA conference,…
The fourth international workshop on Computational Models for Cell Processes (CompMod 2013) took place on June 11, 2013 at the {\AA}bo Akademi University, Turku, Finland, in conjunction with iFM 2013. The first edition of the workshop…
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and…
Mechanics-related problems often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these…
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review…
In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean…
This volume contains the proceedings of MARS 2022, the fifth workshop on Models for Formal Analysis of Real Systems, held as part of ETAPS 2022, the European Joint Conferences on Theory and Practice of Software. The MARS workshops bring…
The special theme of DCM 2009, co-located with ICALP 2009, concerned Computational Models From Nature, with a particular emphasis on computational models derived from physics and biology. The intention was to bring together different…
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose…
Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose vertices is represented by a vector or a single value, limited their representing capability to describe complex objects. In this paper, we propose the first…
This volume contains the accepted papers at the third Workshop on Membrane Computing and Biologically Inspired Process Calculi, held in Bologna on 5th September 2009. The papers are devoted to both membrane computing and biologically…
Graphs may be used to represent many different problem domains -- a concrete example is that of detecting communities in social networks, which are represented as graphs. With big data and more sophisticated applications becoming widespread…
This volume contains the proceedings of MARS 2020, the fourth workshop on Models for Formal Analysis of Real Systems held as part of ETAPS 2020, the European Joint Conferences on Theory and Practice of Software. The MARS workshop brings…
The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the…
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the…
This volume contains the proceedings of EXPRESS/SOS 2019: the Combined 26th International Workshop on Expressiveness in Concurrency and the 16th Workshop on Structural Operational Semantics, which was held on August 26, 2019, in Amsterdam…
The aim of the workshop series Developments in Computational Models (DCM) is to bring together researchers who are currently developing new computational models or new features for traditional computational models, in order to foster their…