**Multi-Resolution Graph Neural Network (MR-GNN)**

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods. … **Deep Logic Model**

Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning. … **Evolving Intelligent System (EIS)**

The term Evolving was first used to describe an intelligent system in 1996 by B. Carse, T. Fogarty and A Munro for a fuzzy rule-based controller where its parameters and structure were learnt simultaneously using a Genetic Algorithm. Years later, alternative methods to learn an evolving intelligent system (EIS) via Incremental learning were suggested as a neuro-fuzzy algorithm by N. Kasabov in 1998 and a rule-based model by P. Angelov in 1999. EIS are usually associated with, streaming data and on-line (often real-time) modes of operation. They can be seen as adaptive intelligent systems. EIS assumes on-line adaptation of system structure in addition to the parameter adaptation which is usually associated with the term ‘incremental’ from Incremental learning. They have been studied as a methodological solution to learn from streaming data exhibiting non-stationary behaviours by M. Sayed-Mouchaweh and E. Lughofer. An important sub-area of EIS is represented by Evolving Fuzzy Systems (EFS) (a comprehensive survey written by E. Lughofer including real-world applications can be found in ), which rely on fuzzy systems architecture and incrementally update, evolve and prune fuzzy sets and fuzzy rules on demand and on-the-fly. One of the major strengths of EFS, compared to other forms of evolving system models, is that they are able to support some sort of interpretability and understandability for experts and users. This opens possibilities for enriched human-machine interaction’s scenarios, where the users may ‘communicate’ with an on-line evolving system in form of knowledge exchange (active learning (machine learning) and teaching). This concept is currently motivated and discussed in the evolving systems community under the term Human-Inspired Evolving Machines and respected as ‘one future’ generation of ‘EIS’.

➘ “PANFIS++” … **PinSage**

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures. …

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