We introduce AutoGraph, a scalable autoregressive model for attributed graph generation using decoder-only transformers. By flattening graphs into random sequences of tokens through a reversible process, AutoGraph enables modeling graphs as sequences without relying on additional node features that are expensive to compute, in contrast to diffusion-based approaches.

On the other hand, in the latter model, the Python interpreter embeds DL operations into a symbolic graph that represents the entire dataflow of aDNN. Thus, users should define their DL programs only with existing symbolic operations thatDLframeworks support.

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph construction stage. Earlier works for graph construction usually rely on speciffic rules or crowdsourcing, which are either too simplistic or too labor-intensive. Recent works start to utilize large language models (LLMs) to automate the graph construction, in view of their abundant open-world knowledge and remarkable reasoning capabilities. Nevertheless, they generally suffer from two limitations: (1) invisibility of global view (e.g., overlooking contextual information) and (2) construction inefficiency. To this end, we introduce AutoGraph, an automatic graph construction framework based on LLMs for recommendation. Specifically, we first use LLMs to infer the user preference and item knowledge, which is encoded as semantic vectors. Next, we employ vector quantization to extract the latent factors from the semantic vectors. The latent factors are then incorporated as extra nodes to link the user/item nodes, resulting in a graph with in-depth global-view semantics. We further design metapath-based message aggregation to effectively aggregate the semantic and collaborative information. The framework is model-agnostic and compatible with different backbone models. Extensive experiments on three real-world datasets demonstrate the efficacy and efffciency of AutoGraph compared to existing baseline methods. We have deployed AutoGraph in Huawei advertising platform, and gain a 2.69% improvement on RPM and a 7.31% improvement on eCPM in the online A/B test. Currently AutoGraph has been used as the main trafffc model, serving hundreds of millions of people.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Originally, TensorFlow only allowed you to code in Graph Mode, but since the ability to code in Eager Mode was introduced, most notebooks produced are in Eager Mode.

If you would like to execute Python code during each invocation of a Function, tf.py_function is an exit hatch. The drawback of tf.py_function is that it's not portable or particularly performant, cannot be saved with SavedModel, and does not work well in distributed (multi-GPU, TPU) setups. Also, since tf.py_function has to be wired into the graph, it casts all inputs/outputs to tensors. Changing Python global and free variables counts as a Python side effect, so it only happens during tracing. Sometimes unexpected behaviors are very hard to notice.

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The rapid evolution of deep neural networks (DNNs) has been fueled by the support of deep learning (DL) frameworks like TensorFlow and PyTorch. DL frameworks allow users to build and execute DNNs through Python programming. The standard execution model in DL frameworks is imperative execution: the Python Interpreter executes a DL program just as it treats a regular Python program. Let us go over a simple DL program to grasp the concept. Here, we assume that the condition the Interpreter first evaluates is True.

Indianapolis Colts owner Jim Irsay had the winning bid for a vintage Apple II manual signed and inscribed by Steve Jobs back in 1980. Irsay, who inherited the NFL team from his late father, Bob, in 1997, paid $787,484 for the spiral-bound user's guide, 9 to 5 Mac reported. The 8-bit Apple II was one of the first successful personal home computers, unveiled by Jobs and Apple co-founder Steve Wozniak at the 1977 West Coast Computer Faire. It was sold via online auction on Thursday, alongside a letter to a fan typed and signed by Jobs that went for $479,939, and an working Apple-1 computer that nabbed $464,876. The Apple-1 was discontinued in October 1977, with Jobs and Wozniak offering discounts and trade-ins for the more advanced Apple II.

Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs. To fully utilize the power of the deep neural network, some deep GNNs have been proposed recently. However, the design of deep GNNs requires significant architecture engineering. In this work, we propose a method to automate the deep GNNs design. In our proposed method, we add a new type of skip connection to the GNNs search space to encourage feature reuse and alleviate the vanishing gradient problem. We also allow our evolutionary algorithm to increase the layers of GNNs during the evolution to generate deeper networks. We evaluate our method in the graph node classification task. The experiments show that the GNNs generated by our method can obtain state-of-the-art results in Cora, Citeseer, Pubmed and PPI datasets.