Multiple Hybrid Phase Transition: Bootstrap Percolation on Complex Networks with Communities

Bootstrap percolation is a well-known model to study the spreading of rumors, new products or innovations on social networks. The empirical studies show that community structure is ubiquitous among various social networks. Thus, studying the bootstrap percolation on the complex networks with communities can bring us new and important insights of the spreading dynamics on social networks. It attracts a lot of scientists' attentions recently. In this letter, we study the bootstrap percolation on Erd\H{o}s-R\'{e}nyi networks with communities and observed second order, hybrid (both second and first order) and multiple hybrid phase transitions, which is rare in natural system. Moreover, we have analytically solved this system and obtained the phase diagram, which is further justified well by the corresponding simulations

Friend Ranking in Online Games via Pre-training Edge Transformers

Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.Comment: Accepted by the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023

HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performance for spatio-temporal forecasting. However, the canonical GNNs-based methods only individually model the local graph of meteorological variables per station or the global graph of whole stations, lacking information interaction between meteorological variables in different stations. In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations. An adaptive graph learning layer and spatial graph convolution are employed to construct self-learning graph and study hidden dependency among nodes of variable-level and station-level graph. For capturing temporal pattern, the dilated inception as the backbone of gate temporal convolution is designed to model long and various meteorological trends. Moreover, a dynamic interaction learning is proposed to build bidirectional information passing in hierarchical graph. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method.Comment: Some sections will be modified because of some errors of the experiments and present

Product and Metal Stocks Accumulation of China's Megacities: Patterns, Drivers, and Implications

The rapid urbanization in China since the 1970s has led to an exponential growth of metal stocks (MS) in use in cities. A retrospect on the quantity, quality, and patterns of these MS is a prerequisite for projecting future metal demand, identifying urban mining potentials of metals, and informing sustainable urbanization strategies. Here, we deployed a bottom up stock accounting method to estimate stocks of iron, copper, and aluminum embodied in 51 categories of products and infrastructure across 10 Chinese megacities from 1980 to 2016. We found that the MS in Chinese megacities had reached a level of 2.6-6.3 t/cap (on average 3.7 t/cap for iron, 58 kg/cap for copper, and 151 kg/cap for aluminum) in 2016, which still remained behind the level of western cities or potential saturation level on the country level (e.g., approximately 13 t/cap for iron). Economic development was identified as the most powerful driver for MS growth based on an IPAT decomposition analysis, indicating further increase in MS as China's urbanization and economic growth continues in the next decades. The latecomer cities should therefore explore a wide range of strategies, from urban planning to economy structure to regulations, for a transition toward more "metal-efficient" urbanization pathways