Search Results

Graph Neural Networks (GNNs), as the mainstream graph representation learning method, has demonstrated its effectiveness in learning graph embeddings over benchmark datasets. However, existing GNNs still have limitations in handling real-world graphs in ...

Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive applications, such as real-time ...

Subgraph matching is one of the fundamental problems in graph analytics. Existing methods generate matching orders to guide their search, which consists of a series of extensions. Each time, they extend smaller partial matches into larger ones until all ...

Graph Neural Networks (GNNs) have received significant attention for demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limited devices because of model sizes and scalability constraints imposed by ...

Graph‐structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real‐world systems. As a prevailing model architecture to ...

Recent mobile applications (i.e., apps) have been extensively implanted with paid advertisements that promote other mobile apps, including malware that raises alarming concerns in cybersecurity. Excavating the app promotion patterns in the app-...

The success of GNNs highly relies on the accurate labeling of data. Existing methods of ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing nodes in the graphs. However, in reality, new nodes always continuously ...

The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the various side effects can ...

Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of many of these methods is frequently impeded by resource ...

Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead to reduced performance, known as negative transfer. Unlike in image or ...

Self-supervised learning (SSL) has gained increasing attention in the graph learning community, owing to its capability of enabling powerful models pre-trained on large unlabeled graphs for general purposes, facilitating quick adaptation to specific ...

Among the existing self-supervised learning (SSL) methods for graphs, graph contrastive learning (GCL) frameworks usually automatically generate supervision by transforming the same graph into different views through graph augmentation operations. The ...

Inspired by the success of graph contrastive learning, researchers have begun exploring the benefits of contrastive learning over hypergraphs. However, these works have the following limitations in modeling the high-order relationships over unlabeled ...

Graph Neural Networks (GNNs) have been a prominent research area and have been widely deployed in various high-stakes applications in recent years, leading to a growing demand for explanations. While existing explainer methods focus on explaining ...

Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging ...

Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the ...

Due to the growing importance of using graph neural networks in high-stakes applications, there is a pressing need to interpret the predicted results of these models. Existing methods for explanation have mainly focused on generating sub-graphs ...

Modern machine learning techniques, especially deep neural networks, have demonstrated excellent performance for various knowledge discovery and data mining applications. However, the development of many of these techniques still encounters resource ...

Graph Neural Networks (GNNs) are powerful models for non-Euclidean data, but their training is often accentuated by massive unnecessary computation: On the one hand, training on non-Euclidean data has relatively high computational cost due to its ...

Graph Neural Networks (GNNs) have demonstrated a great representation learning capability on graph data and have been utilized in various downstream applications. However, real-world data in web-based applications (e.g., recommendation and advertising) ...