Graphs are powerful analytic tools for modeling adversarial activities across a wide range of domains and applications. Examples include identifying and responding to cybersecurity systems' threats and vulnerabilities, strengthening critical ...

Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-centric approaches, highlighted by the success of large foundational models. Following this trend, despite numerous innovations in graph machine learning ...

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying ...

Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with overfitting and over-smoothing as they go deeper as models of computer vision (CV) realm. The ...

Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for ...

While graph machine learning, and notably graph neural networks (GNNs), have gained immense traction in recent years, application is predicated on access to a known input graph upon which predictive models can be trained. And indeed, within the most ...

Foundation models such as GPT-4 for natural language processing (NLP), Flamingo for computer vision (CV), have set new benchmarks in AI by delivering state-of-the-art results across various tasks with minimal task-specific data. Despite their success, ...

Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine learning (ML) to automatically ...

The complexity of relationships between entities is increasing in the era of big data, leading to a growing interest in graph (network) data, owing to its ability to encode intricate relational information. Graph data mining and machine learning methods ...

Conventional Artificial Intelligence models are heavily limited in handling complex biomedical structures (such as 2D or 3D protein and molecule structures) and providing interpretations for outputs, which hinders their practical application. Graph ...

Graph neural networks (GNNs) have become core building blocks behind a myriad of graph learning tasks. The vast majority of the existing GNNs are built upon, either implicitly or explicitly, the homophily assumption, which is not always true and could ...

The huge volume of emerging graph datasets has become a double-bladed sword for graph machine learning. On the one hand, it empowers the success of a myriad of graph neural networks (GNNs) with strong empirical performance. On the other hand, training ...

The 19th International Workshop on Mining and Learning with Graphs (MLG) is held in Long Beach, CA, USA and is co-located with the Tenth International Workshop on Deep Learning on Graphs (DLG) as part of the 29th ACM SIGKDD Conference on Knowledge ...

Recent years have witnessed a surge of research interest in graph machine learning. However, the benchmark datasets available to the field are rather limited in both quantity and diversity, an issue particularly notable given the immense potential ...

Graph features are crucial to many applications such as recommender systems and risk management systems. The process to obtain useful graph features involves ingesting data from various upstream data sources, defining the desired graph features for the ...

Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users ...

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have ...

Despite the great success of Graph Machine Learning (GML) in a variety of applications, the industry is still seeking a platform which makes performing industrial-purpose GML convenient. In this demo, we present EasyGML, a fully-functional and easy-to-...