Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

Network embedding is an important method to learn low-dimensional representations of
vertexes in networks, aiming to capture and preserve the network structure. Almost all the …

Image-language matching tasks have recently attracted a lot of attention in the computer
vision field. These tasks include image-sentence matching, ie, given an image query …

This paper proposes a method for learning joint embeddings of images and text using a two-
branch neural network with multiple layers of linear projections followed by nonlinearities …

Attributed network embedding aims to seek low-dimensional vector representations for
nodes in a network, such that original network topological structure and node attribute …

Data embedding is used in many machine learning applications to create low-dimensional
feature representations, which preserves the structure of data points in their original space …

We outline an anomaly detection method for industrial control systems (ICS) that combines
the analysis of network package contents that are transacted between ICS nodes and their …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA and …