Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...

import numpy as np def doSomethingInPython(vec: np.ndarray) -> np.ndarray: """ Accepts a numpy vector and returns a new vector where each element is: sin(x) + log(abs ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

MathWorks, a leading developer of mathematical simulation and computing software, revealed that a ransomware gang stole the data of over 10,000 people after breaching its network in April. The company ...

Rajiv Shesh is the Chief Revenue Officer at HCLSoftware where he leads revenue growth & customer advocacy for Products & Platforms division. What’s really powering AI? High-quality data—foundational ...

For decades, enterprise data infrastructure focused on answering the question: “What happened in our business?” Business intelligence tools, data warehouses, and pipelines were built to surface ...

In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...

What if the tools you already use could do more than you ever imagined? Picture this: you’re working on a massive dataset in Excel, trying to make sense of endless rows and columns. It’s slow, ...

Abstract: The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data ...