Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data. To address this gap, we introduce Textual-Edge Graphs Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of benchmark textual-edge datasets featuring rich textual descriptions on nodes and edges. The TEG-DB datasets are large-scale and encompass a wide range of domains, from citation networks to social networks. In addition, we conduct extensive benchmark experiments on TEG-DB to assess the extent to which current techniques, including pre-trained language models, graph neural networks, and their combinations, can utilize textual node and edge information. Our goal is to elicit advancements in textual-edge graph research, specifically in developing methodologies that exploit rich textual node and edge descriptions to enhance graph analysis and provide deeper insights into complex real-world networks.

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools.

Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, with edges typically represented by mere binary or categorical attributes. This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data. To address this gap, we introduce Textual-Edge Graphs Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of benchmark textual-edge datasets featuring rich textual descriptions on nodes and edges. The TEG-DB datasets are large-scale and encompass a wide range of domains, from citation networks to social networks.

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools.

To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human -readable persona modeling. In dynamic real -world scenarios, effective persona modeling necessitates leveraging streaming behavior data to continually optimize user personas. However, existing methods -whether regenerating personas or incrementally extending them with new behaviors -often fail to achieve sustained improvements in persona quality or future behavior prediction accuracy. To address this, we propose DEEPER, a novel approach for dynamic persona modeling that enables continual persona optimization. Specifically, we enhance the model's direction -search capability through an iterative reinforcement learning framework, allowing it to automatically identify effective update directions and optimize personas using discrepancies between user behaviors and model predictions. Extensive experiments on dynamic persona modeling involving 4800 users across 10 domains highlight the superior persona optimization capabilities of DEEPER, delivering an impressive 32.2% average reduction in user behavior prediction error over four update rounds -outperforming the best baseline by a remarkable 22.92%.

Quanergy Systems, Inc., a leading provider of LiDAR sensors and smart 3D solutions, announced that Prime Secured will use Quanergy's products and software to provide its customers with greater physical security and flow management capabilities. "We've been very impressed with the performance of Quanergy's products and solutions – and the wide array of applications. With Quanergy's technology, we're able to provide our customers in the gaming space with an analytics platform that gives them deeper insight into activity at their facilities." As the company looks to expand into new industries such as gaming, Prime Secured has placed a multi-million dollar order with Quanergy that will begin with a deployment at a major casino. The gaming facility sought a fully automated, more efficient and more productive physical protection platform.

It is safe to say that today's marketing technologies present remarkable opportunities as well as enormous challenges. On the one hand, it is possible to scale campaigns and reach consumers in ways that were once unimaginable. On the other hand, consumers feel increasingly numb to the onslaught of messages and content they receive. As a result, they often disregard emails, promoted posts, and pop-ups without even glancing at them. At the heart of all this is a basic fact: it is incredibly easy and relatively inexpensive to flood consumers with digital messaging, but a scattershot approach, even if it's cheap, isn't necessarily better.

Raviteja Dodda (Ravi) is the Co-Founder and CEO of MoEngage, an insights-led customer engagement platform. Artificial intelligence (AI) has revolutionized many industries, and marketing is one of the most compelling examples. AI has breathed new life into marketing by providing deeper insights into customer behavior, allowing marketers to build loyalty through improved user experience and tailored communications. But the typical marketing technology stack makes it challenging for brands to leverage data properly. Years of add-on technology investments have left customer data in silos, scattered across the organization.

It doesn't generate revenue, but it is an essential part of operating effectively as a business today. Whether it's industry specific regulations, or the standout regulation of our time--GDPR--we are all acutely aware of the damage, both reputational and financial, that non-compliance can cause. GDPR has equipped employees across industries with an appreciation of the context, usage, and security of data, but there is another factor that is essential for establishing an effective data strategy, which is data discoverability. To ensure regulatory compliance, data must not only be secure, it must also be discoverable so that compliance personnel can locate all information needed to prove compliance. Increasingly, AI tools are being harnessed to automate workflows and governance, but such capabilities can only be delivered when a strong data foundation is in place.

The networking space has evolved dramatically over the last two years as organizations realize the increasing value of AIOps, the benefits of full network visibility, and the role secure access plays with remote workforces. Network analytics and monitoring procedures that once were considered standard have quickly becoming inadequate in today's rapidly changing IT network landscape. But fortunately, advanced networking analytics leveraging Artificial Intelligence (AI) and Machine Learning (ML) are helping to overcome new challenges when it comes to maintaining network performance and future-proofing NetOps teams. As a quick refresher, network analytics applies data analytic techniques to network data to monitor complete network behavior. With the addition of AI/ML technologies (and the rise of AIOps), deeper insights into application and network performance can be drawn on network data.