The proposed research on pre-training temporal graph neural networks across multiple networks has the potential to advance the field of machine learning and its applications significantly. By introducing methodologies to enhance the scalability and transferability of TGNNs, this work could revolutionize areas like network security, financial fraud detection, and real-time social network analysis, where dynamic and adaptive models are essential. The publicly available dataset of 84 Ethereum-based temporal networks will serve as a valuable resource for the research community, fostering innovation and collaboration. Furthermore, the principles of multi-network pre-training introduced here can inspire analogous advances in other temporal data domains, such as healthcare, transportation, and climate science. This research opens up a new direction in training generalizable temporal graph models that, for the first time, can be trained on distinct temporal networks, paving the way for Temporal Graph Foundation Models. This work also introduces a set of Ethereum transaction token networks, which are publicly available to users who have the necessary resources, such as fast SSDs, large RAM, and ample disk space, to synchronize Ethereum clients and manually extract blocks. Additionally, all Ethereum data is accessible on numerous Ethereum explorer sites such as etherscan.io. An Ethereum user's privacy depends on whether personally identifiable information (PII) is associated with any of their blockchain address, which serves as account handles and are considered pseudonymous. If such PII were obtained from other sources, our datasets could potentially be used to link Ethereum addresses. However, real-life identities can only be discovered using IP tracking information, which we neither have nor share. Our data does not contain any PII. Furthermore, we have developed a request to exclude an address from the dataset. Benchmark datasets have become fundamental for advancing graph machine learning, providing a common ground to evaluate models and facilitate the development of graph foundation models. Early graph ML studies often relied on a handful of small, static benchmark graphs (e.g., citation networks like Cora/Citeseer and molecular graphs from the TU collection [37]).

Why does Amazon have no Western rivals? Vitamins, repair tape and a jar of mango chutney - just some of what my household bought last month via Amazon's sprawling online shopping platform. We also shopped at the company's supermarket chain Whole Foods, streamed its TV shows, read books on Kindle e-readers, and browsed countless websites no doubt powered by Amazon Web Services (AWS), its highly profitable cloud-computing business. And that isn't half of the interconnected products and services offered by the global behemoth, which earlier this year overtook US superstore giant Walmart to become the world's largest company by annual sales. But why does Amazon, launched by Jeff Bezos in 1995 as an online bookstore out of a rented garage, have so few serious rivals in the West when it comes to e-commerce?

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Toyota's CUE7 robot shoots hoops using AI You don't need an SSN to open a credit card: Scammers know that Mexico's climate supercomputer could change forecasting Michael Easter and Gary Brecka discuss the'choice' to live to be 100 'CyberGuy' warns of creepy privacy clauses in smart devices Brian Oliver of Gainesville, Florida, spoke with Kurt CyberGuy Knutsson about losing money to scammers claiming to be with PayPal. NEW You can now listen to Fox News articles! Brian Oliver is retired, sharp and financially savvy enough to have a stock-and-bond portfolio worth hundreds of thousands of dollars. He is not the type of person you picture getting scammed.

Recommendation and collaborative filtering systems are important in modern information and e-commerce applications. As these systems are becoming increasingly popular in the industry, their outputs could affect business decision making, introducing incentives for an adversarial party to compromise the availability or integrity of such systems. We introduce a data poisoning attack on collaborative filtering systems. We demonstrate how a powerful attacker with full knowledge of the learner can generate malicious data so as to maximize his/her malicious objectives, while at the same time mimicking normal user behavior to avoid being detected. While the complete knowledge assumption seems extreme, it enables a robust assessment of the vulnerability of collaborative filtering schemes to highly motivated attacks.

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant.

After OpenAI's Instant Checkout feature fell short, Walmart is instead embedding its Sparky chatbot directly into ChatGPT and Google Gemini. Since November, Walmart has let some ChatGPT users order a limited selection of products without ever leaving OpenAI's chatbot interface. Sales have been disappointing, a Walmart executive vice president exclusively tells WIRED. The results suggest that a future where chatbots and AI agents take over ecommerce is still a way off, if it ever materializes. Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT.

Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Most of the account takeover scams reported last year were for mobiles, online shopping and credit cards, Cifas said. Criminals are increasingly exploiting AI technology to take over people's mobile, banking and online shopping accounts, the UK's leading anti-fraud body has warned. Last year, a record number of scams were reported to the national fraud database, fuelled by AI, which allows for large-scale deception on "industrialised" levels, according to Cifas, the fraud prevention organisation. Its report showed 444,000 cases of fraud were reported by its members last year - a 6% increase on 2024.