arXiv subjects

Feiyang Wang

Publications and source records attributed to Feiyang Wang.

23 records · Page 2Linked to original sources

arXiv2022

Exploring the relationship between built environment and bike-sharing demand: Does the trip length matter?

Bike-sharing has received considerable practice and research attention over the past decade. As a manpower-driven transportation mode, it seems more sensitive to trip length, since one could take a shared bike to a destinated place where is too far to walk, or choose it for simply replacing walking when going to a nearby place. However, little research has paid attention to it, i.e., the differentiated effects of built environment on the bike-sharing demand with trip lengths. To fill the gap, this paper identifies a threshold of bike-sharing trip lengths from bike-sharing trace data, and employs a semiparametric geographically weighted Poisson regression (SGWPR) model to investigate the relationship between built environment and bike-sharing demand with different lengths considering the heterogeneity in the relationship. The results show that built environment has heterogeneous effects on the bike-sharing demand in urban areas, and the effects differ across groups with trip lengths. The findings contribute to understanding the relationships between built environment and bike-sharing demand, and providing supports for the placements and dispatchment of shared bikes.

physics.soc-ph↗

arXiv2022

PERFECT: A Hyperbolic Embedding for Joint User and Community Alignment

Social network alignment shows fundamental importance in a wide spectrum of applications. To the best of our knowledge, existing studies mainly focus on network alignment at the individual user level, requiring abundant common information between shared individual users. For the networks that cannot meet such requirements, social community structures actually provide complementary and critical information at a slightly coarse-grained level, alignment of which will provide additional information for user alignment. In turn, user alignment also reveals more clues for community alignment. Hence, in this paper, we introduce the problem of joint social network alignment, which aims to align users and communities across social networks simultaneously. Key challenges lie in that 1) how to learn the representations of both users and communities, and 2) how to make user alignment and community alignment benefit from each other. To address these challenges, we first elaborate on the characteristics of real-world networks with the notion of delta-hyperbolicity, and show the superiority of hyperbolic space for representing social networks. Then, we present a novel hyperbolic embedding approach for the joint social network alignment, referred to as PERFECT, in a unified optimization. Extensive experiments on real-world datasets show the superiority of PERFECT in both user alignment and community alignment.

cs.SI↗

arXiv2021

Data-driven Smart Ponzi Scheme Detection

A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Experimental results show that this method is significantly better than the existing smart Ponzi scheme detection methods.

cs.LG↗

arXiv2021

Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets.

cs.LG↗

arXiv2019

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.

cs.CL↗