Graph neural networks (GNNs) are powerful models for processing graph data and have demonstrated state-of-the-art performance on many downstream tasks. However, existing GNNs can generally suffer from two limitations: over-smoothing and over-squashing, which can significantly undermine their learning ability for large graphs. To overcome these issues simultaneously, by utilizing the concept of effective resistances, we focus on minimizing total constrained resistance while identifying problematic edges using topological redundancy and bottleneck sparsity coefficients. We introduce a novel graph rewiring and preprocessing method guided by effective resistance (GPER), capable of edge addition or removal. Theoretical analysis validates our method's efficacy in mitigating over-smoothing and over-squashing. In the experiments, we conduct node and graph classifications on the benchmark datasets and can achieve an average improvement of 7.8% and 2.0%, respectively. We also conduct scalability analysis on large graphs with GCN and demonstrate that the proposed preprocess approach can reduce graph size by over 50% while improve the performance.

With the advent of big data and major technological achievements in information and communications technology, abundant human trajectory data are collected by location-aware devices such as mobile phones. This information has spurred the development of several methods for measuring spatial interaction, such as the gravity model and radiation model. However, prior studies have mainly measured the scale of spatial interaction at an aggregated level and have derived static or unidirectional interaction. As an improvement, an individual differentiated multidimensional (IDMD) Hawkes model, which can capture individual mobility differences as well as the dynamic mutual interaction between geographical units, is proposed herein. Internet protocol detail records (IPDRs) of Jinhua, Zhejiang, China, covering 23 days, are used as a case study to infer the spatial structure of urban interaction. Compared to the traditional spatial interaction model, the IDMD Hawkes model demonstrates unique advantages in capturing spatial interactions. The results reveal the level of interaction between regions and identify the closely related districts; this can be verified by the economic exchange and transportation convenience between these regions. Our approach provides a novel perspective for measuring spatial interaction considering individual mobility, which can assist policymakers to better understand the spatial structure of a city and plan an efficient city configuration.

User portrait analysis is one of the key points in human behavior analysis. It is important to describe or guess user's characteristics through rational methods in business analysis. In this paper, we use the user details records data set from a mobile operator to analyze the preference of users with different brand phones for different APPs and propose the concept of mobile Internet life personas (MILP) and the latent MILP indexing (LMILPI) model for the analysis of users' MILP. At the same time, we build user portrait analysis framework based on the latent semantic indexing (LSI) theme model, LMILPI model, and association rule mining. On the one hand, we analyze users' preference for APP content when using different mobile brands. On the other hand, we analyze the relationship among mobile brands, user access time, and MILPs to describe users' Internet behavior. Our research shows that there is a difference between users who use different brands of mobile phones: 1) users who use different brand phones have different preferences for different APPs. However, if mobile brand marketing methods or target users are same or similar, the APP preference of these brands will be similar; 2) MILPs are different between users who use different brands of mobile phones, but the MILPs displayed on Android platform are similar even though brands are not same, while the MILP displayed on iPhone is quite different from MILPs on Android; and 3) MILPs' importance will be changed by mobile phone brands and time periods. The analytical framework which we propose can provide commercial solutions such as application recommendations, market strategy formulation, Internet access, and other fields.

In large-scale mobile networks, energy minimization problem pertaining to base station (BS) switching is known to require high computational complexity. In this paper, we propose a data-driven energy-saving (DDE) framework which partitions the networks into communities. Each community can separately make decisions with low computational cost. We introduce a novel metric to measure the collaborative relationship among BSs. Then, the mobile networks are partitioned via a multiscale community detection method. To improve the cell zooming performance, we estimate the aggregate traffic demands of the communities based on the historical traffic profile. A heuristic switch-off strategy is proposed to maximize the energy savings while guaranteeing minimal service requirements. Experiments with two real regional cellular network datasets show that the proposed DDE framework can conserve a significant amount of energy and leverage the tradeoff between energy savings and the blockage ratio.