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]).

This material may not be published, broadcast, rewritten, or redistributed.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 . China's brain chip breakthrough raises big questions Should you change your phone number after a hack? McDonald's AI drive-thru may take your next order The Father's Day gift that protects your dad from scammers New Air Force One'flying palace' gifted by Qatar unveiled for President Trump Kevin O'Leary warns U.S. must accelerate data center growth to keep pace with China in AI race Americans need protection against'warrantless surveillance': Rep Chip Roy Spencer Pratt's use of AI to boost campaign sparks debate China approves world's first commercial brain chip Crypto kiosks helped scammers turn fear into fast cash.

We propose BlockScan, a customized Transformer for anomaly detection in blockchain transactions. Unlike existing methods that rely on rule-based systems or directly apply off-the-shelf large language models (LLMs), BlockScan introduces a series of customized designs to effectively model the unique data structure of blockchain transactions. First, a blockchain transaction is multi-modal, containing blockchain-specific tokens, texts, and numbers. We design a novel modularized tokenizer to handle these multi-modal inputs, balancing the information across different modalities. Second, we design a customized masked language modeling mechanism for pretraining the Transformer architecture, incorporating RoPE embedding and FlashAttention for handling longer sequences. Finally, we design a novel anomaly detection method based on the model outputs.

Pump.Fun's Bounties Platform Is a Black Hole of Circular Grifting The crypto platform claims you can "pay anyone to do anything," from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other. Would you run into a crowded university lecture hall, fart into a megaphone, and bellow "fartcoin" at the top of your lungs? If so--and should you have the means to document this stunt on video, preferably capturing the audience's reaction--you may claim a reward of approximately $1,000 . The money, of course, will be dispensed in fartcoin, a meme cryptocurrency trading at a little over 10 cents at time of publication, with a total market capitalization hovering around $130 million. Such is the promise of Pump.Fun GO, a new feature on Pump.Fun, one of the fastest-growing crypto businesses of the past few years.

They swear they haven't peeked at the closely guarded secret and that they'll keep the cryptographic competition going. On a blustery March day, the artist Jim Sanborn received visitors at his studio on an isolated island in the Chesapeake Bay. The visitors sat him down in front of a laptop, and he typed in a secret message. They compressed the message using a unique hash function, sent that to the cloud, and wiped the laptop clean. Sanborn hoped that this action would set him free.

Since its 2009 genesis block, the Bitcoin network has processed >1.08 billion (B) transactions representing >8.72B BTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its UTxO-based design, have rendered this data largely inaccessible for ML research. Addressing this gap, we present an ML-compatible graph modeling the Bitcoin's economic topology by reconstructing the flow of funds. This temporal, heterogeneous graph encompasses complete transaction history up to block 863000, consisting of >2.4B nodes and >39.72B edges. Additionally, we provide custom sampling methods yielding node and edge feature vectors of sampled communities, tools to load and analyze the Bitcoin graph data within specialized graph databases, and ready-to-use database snapshots. This comprehensive dataset and toolkit empower the ML community to tackle Bitcoin's intricate ecosystem at scale, driving progress in applications such as anomaly detection, address classification, market analysis, and large-scale graph ML benchmarking. Dataset and code available at https://github.com/B1AAB/EBA.

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?

Some critics say the increasing use of AI could result in'asynchronous, agent-driven management'. Some critics say the increasing use of AI could result in'asynchronous, agent-driven management'. 'I didn't want to be the guinea pig': inside tech's AI-fueled manager purge As tech companies pour billions into artificial intelligence bets and slash their workforces, middle managers are squarely in the crosshairs. A trend is emerging: when tech CEOs announce that AI is making it possible to do more with fewer workers, they promise to flatten their structures by cutting away what they call unnecessary management layers and bureaucracy. Just last week, the cryptocurrency exchange Coinbase laid off 14% of its workforce while gesturing to the thrill of AI-fueled, minimal-management efficiency.

Truth Predict was supposed to be the Trump family's biggest leap yet into prediction markets. Now it's looking more like a tiptoe. The odds that the Trump family will launch a full-fledged prediction market product this year just plummeted. Last year, the Trump Media and Technology Group announced Truth Predict, a partnership with the cryptocurrency company Crypto.com. The initial announcement touted Truth Predict as a "new product" that would allow Truth Social users to make trades on sports, inflation, elections, and more through an "embedded" prediction market service.

This story appears in the June 2026 print edition. While some stories from this issue are not yet available to read online, you can explore more from the magazine . Get our editors' guide to what matters in the world, delivered to your inbox every weekday. The courtship between Silicon Valley and MAGA was consummated on June 6, 2024, in San Francisco's Pacific Heights neighborhood, on a street known as "Billionaires' Row," at the 22,000-square-foot, $45 million French-limestone mansion of a venture capitalist named David Sacks. Along with Chamath Palihapitiya, a fellow venture capitalist and a colleague on the podcast, Sacks hosted a fundraiser for Donald Trump. He knew that other technology titans were coming around to the ex-president but remained in the closet. "And I think that this event is going to break the ice on that," Sacks said on the podcast the week before the fundraiser. "And maybe it'll create a preference cascade, where all of a sudden it becomes acceptable to acknowledge the truth." Check out more from this issue and find your next story to read. A few years earlier, Sacks had described the January 6, 2021, riot at the U.S. Capitol as an "insurrection" and pronounced Trump "disqualified" from ever again holding national office. "What Trump did was absolutely outrageous, and I think it brought him to an ignominious end in American politics," he said on the podcast a few days after the event. "He will pay for it in the history books, if not in a court of law." Palihapitiya was more colloquial, calling Trump "a complete piece-of-shit fucking scumbag." These might seem like tricky positions to climb down from--but the path that leads from scathing denunciation through gradual accommodation to sycophantic embrace of Trump is a well-worn pilgrimage trail. The journey is less wearisome for self-mortifiers who never considered democracy (a word seldom spoken on the podcast) all that important in the first place.