• arXiv.cs.SI Pub Date : 2020-07-14
Robert L. Peach; Sam F. Greenbury; Iain G. Johnston; Sophia N. Yaliraki; David Lefevre; Mauricio Barahona

The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-14
Chenming Yang; Liang Zhou; Hui Wen; Zhiheng Zhou; Yue Wu

Detecting anomalous edges and nodes in dynamic networks is critical in various areas, such as social media, computer networks, and so on. Recent approaches leverage network embedding technique to learn how to generate node representations for normal training samples and detect anomalies deviated from normal patterns. However, most existing network embedding approaches learn deterministic node representations

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-14
Huaishao Luo; Chuishi Meng; Bowen Wu; Junbo Zhang; Tianrui Li; Yu Zheng

The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies are road-based or grid-based, which usually causes the data sparsity problem and affects the detection results. In this paper, we proposed a dynamic region partition method to address the above issues. Besides, we proposed

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-13

Social Network Analysis (SNA) has shed powerful light on cultures where the influence of patronage, preferment, and reciprocal obligations are traditionally important. Accordingly, we argue here that episcopal appointments, culture, and governance within the Catholic Church are ideal topics for SNA interrogation. We analyse original network data for the Catholic Bishops' Conference of England and Wales

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-14
Kenan Huremovic; Ali Ozkes

We introduce a model of polarization in networks as a unifying framework for the measurement of polarization that covers a wide range of applications. We consider a sufficiently general setup for this purpose: node- and edge-weighted, undirected, and connected networks. We generalize the axiomatic characterization of Esteban and Ray (1994) and show that only a particular instance within this class

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-14
Yuntian Deng; Hao Chen; Shiping Shao; Jiacheng Tang; Jianzong Pi; Abhishek Gupta

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric demand, over a large city with multiple nodes (representing neighborhoods). We assume that the passengers queue up at every

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-13
Chiara Ravazzi; Fabrizio Dabbene; Constantino Lagoa; Anton V. Proskurnikov

Interpersonal influence estimation from empirical data is a central challenge in the study of social structures and dynamics. Opinion dynamics theory is a young interdisciplinary science that studies opinion formation in social networks and has a huge potential in applications, such as marketing, advertisement and recommendations. The term social influence refers to the behavioral change of individuals

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-13
Yuntian Deng; Abhishek Gupta; Ness B. Shroff

In this paper, we propose a closed queueing network model for performance analysis of electric vehicle sharing systems with a certain number of chargers in each neighborhood. Depending on the demand distribution, we devise algorithms to compute the optimal fleet size and number of chargers required to maximize profit while maintaining a certain quality of service. We show that the profit is concave

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-09
Florence Regol; Soumyasundar Pal; Mark Coates

Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In particular, corruptions of the graph topology can degrade the performance of graph based learning algorithms severely. This is due to the fact that the

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-13
Christoph Gote; Giona Casiraghi; Frank Schweitzer; Ingo Scholtes

We propose a novel sequence prediction method for sequential data capturing node traversals in graphs. Our method builds on a statistical modelling framework that combines multiple higher-order network models into a single multi-order model. We develop a technique to fit such multi-order models in empirical sequential data and to select the optimal maximum order. Our framework facilitates both next-element

更新日期：2020-07-15
• arXiv.cs.SI Pub Date : 2020-07-13
Mateusz Wilinski; Andrey Y. Lokhov

Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing, and opinion setting. Recent real-world spreading events further highlight the need for prediction, optimization, and control of diffusion dynamics. To tackle these tasks, it is essential to learn the effective spreading model and transmission probabilities across the network

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-13
William Brown; Utkarsh Patange

We consider a setting where individuals interact in a network, each choosing actions which optimize utility as a function of neighbors' actions. A central authority aiming to maximize social welfare at equilibrium can intervene by paying some cost to shift individual incentives, and the optimal intervention can be computed using the spectral decomposition of the graph, yet this is infeasible in practice

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-12
Raiyan Abdul Baten; Gourab Ghoshal; Mohammed Ehsan Hoque

As the world braces itself for a pandemic-induced surge in automation and a consequent (accelerated) shift in the nature of jobs, it is essential now more than ever to understand how people's creative performances are impacted by their interactions with peers in a social network. However, when it comes to creative ideation, it is unclear how the demographic cues of one's peers can influence the network

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-13
Jiaxuan You; Jure Leskovec; Kaiming He; Saining Xie

Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-13
Yoav Kolumbus; Noam Nisan

We study the effectiveness of tracking and testing in mitigating or suppressing epidemic outbreaks, in combination with or as an alternative to quarantines and global lockdowns. We study these intervention methods on a network-based SEIR model, augmented with an additional probability to model symptomatic, asymptomatic and pre-symptomatic cases. Our focus is on the basic trade-offs between economic

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-12
Muhammad Baqer Mollah; Jun Zhao; Dusit Niyato; Yong Liang Guan; Chau Yuen; Sumei Sun; Kwok-Yan Lam; Leong Hai Koh

Internet of Vehicles (IoV) is an emerging concept that is believed to help realise the vision of intelligent transportation systems (ITS). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, Internet of Things and cyber-physical systems. IoV enables the integration of smart

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-12
An Zeng; Ying Fan; Zengru Di; Yougui Wang; Shlomo Havlin

Teamwork is one of the most prominent features in modern science. It is now well-understood that the team size is an important factor that affects team creativity. However, the crucial question of how the character of research studies is influenced by the freshness of the team remains unclear. In this paper, we quantify the team freshness according to the absent of prior collaboration among team members

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-12
Mousumi Karmakar; Sumit Kumar Banshal; Vivek Kumar Singh

Social media platforms have now emerged as an important medium for wider dissemination of research articles; with authors, readers and publishers creating different kinds of social media activity about the article. Some research studies have even shown that articles that get more social media attention may get higher visibility and citations. These factors are now persuading journal publishers to integrate

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-11
Claudia Flores-Saviaga; Saiph Savage

Social media platforms have been extensively used during natural disasters. However, most prior work has lacked focus on studying their usage during disasters in the Global South, where Internet access and social media utilization differs from developing countries. In this paper, we study how social media was used in the aftermath of the 7.1-magnitude earthquake that hit Mexico on September 19 of 2017

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-11
Jiajun Zhou; Jie Shen; Shanqing Yu; Guanrong Che; Qi Xuan

Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale in the benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. To improve this, we introduce data augmentation on graphs (i.e. graph augmentation)

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-10
Gautam Mahapatra; Priodyuti~Pradhan; Ranjan Chattaraj; Soumya Banerjee

Digital contact tracing of an infected person, testing the possible infection for the contacted persons, and isolation play a crucial role in alleviating the outbreak. Here, we design a dynamic graph streaming algorithm that can trace the contacts under the control of the Public Health Authorities (PHA). The algorithm can work as the augmented part of the PHA for the crisis period. Our algorithm receives

更新日期：2020-07-14
• arXiv.cs.SI Pub Date : 2020-07-10
Sjouke Mauw; Yunior Ramírez-Cruz; Rolando Trujillo-Rasua

This paper addresses active re-identification attacks in the context of privacy-preserving social graph publication. Active attacks are those where the adversary can leverage fake accounts, a.k.a. sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. In this paper we present a new probabilistic interpretation of this type of attacks. Unlike

更新日期：2020-07-13
• arXiv.cs.SI Pub Date : 2020-07-10
Louis M Shekhtman; Alon Sela; Shlomo Havlin

We consider the privacy of interactions between individuals in a network. For many networks, while nodes are anonymous to outside observers, the existence of a link between individuals implies the possibility of one node revealing identifying information about its neighbor. Moreover, while the identities of the accounts are likely hidden to an observer, the network of interaction between two anonymous

更新日期：2020-07-13
• arXiv.cs.SI Pub Date : 2020-07-10
Ginestra Bianconi; Hanlin Sun; Giacomo Rapisardi; Alex Arenas

With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. At the mitigation stage of the epidemics in which several countries are now, the use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation

更新日期：2020-07-13
• arXiv.cs.SI Pub Date : 2020-07-10
A. -R. Lagos; I. Kordonis; G. P. Papavassilopoulos

The spontaneous behavioral changes of the agents during an epidemic can have significant effects on the delay and the prevalence of its spread. In this work, we study a social distancing game among the agents of a population, who determine their social interactions during the spread of an epidemic. The interconnections between the agents are modeled by a network and local interactions are considered

更新日期：2020-07-13
• arXiv.cs.SI Pub Date : 2020-07-10
Rachit Agarwal; Shikhar Barve; Sandeep Kuman Shukla

The temporal nature of modeling accounts as nodes and transactions as directed edges in a directed graph -- for a blockchain, enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features

更新日期：2020-07-13
• arXiv.cs.SI Pub Date : 2020-07-09
Disheng Tang; Wenbo Du; Louis Shekhtman; Yijie Wang; Shlomo Havlin; Xianbin Cao; Gang Yan

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidences have shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-09
Massimiliano Mattetti; Akihiro Kishimoto; Adi Botea; Elizabeth Daly; Inge Vejsbjerg; Bei Chen; Öznur Alkan

Understanding prospective clients becomes increasingly important as companies aim to enlarge their market bases. Traditional approaches typically treat each client in isolation, either studying its interactions or similarities with existing clients. We propose the Client Network, which considers the entire client ecosystem to predict the success of sale pitches for targeted clients by complex network

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-09
Takanori Fujiwara; Tzu-Ping Liu

Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional political data (e.g., roll-call votes and surveys) for use in analysis and visualization. These methods often work by finding the directions or principal components (PCs) on which either the data varies the most or respondents make the fewest decision errors. However, these

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-08
Mari Kawakatsu; Philip S. Chodrow; Nicole Eikmeier; Daniel B. Larremore

Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-08
F. O. Bunnin; A. Shenvi; J. Q. Smith

The threat status and criminal collaborations of potential terrorists are hidden but give rise to observable behaviours and communications. Terrorists, when acting in concert, need to communicate to organise their plots. The authorities utilise such observable behaviour and communication data to inform their investigations and policing. We present a dynamic latent network model that integrates real-time

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-02
Daniel BoskKTH; Yérom-David BrombergWIDE, IRISA; Sonja BucheggerKTH; Adrien LuxeyWIDE, IRISA; François TaïaniWIDE, IRISA

Mass surveillance of the population by state agencies and corporate parties is now a well-known fact. Journalists and whistle-blowers still lack means to circumvent global spying for the sake of their investigations. With Spores, we propose a way for journalists and their sources to plan a posteriori file exchanges when they physically meet. We leverage on the multiplication of personal devices per

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-09
Alejandro Carballosa; Mariamo Mussa-Juane; Alberto P. Muñuzuri

Attempts to control the epidemic spread of COVID19 in the different countries often involve imposing restrictions to the mobility of citizens. Recent examples demonstrate that the effectiveness of these policies strongly depends on the willingness of the population to adhere them. And this is a parameter that it is difficult to measure and control. We demonstrate in this manuscript a systematic way

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-08
Suman Banerjee; Bithika Pal

Given a temporal network $\mathcal{G}(\mathcal{V}, \mathcal{E}, \mathcal{T})$, $(\mathcal{X},[t_a,t_b])$ (where $\mathcal{X} \subseteq \mathcal{V}(\mathcal{G})$ and $[t_a,t_b] \subseteq \mathcal{T}$) is said to be a $(\Delta, \gamma)$\mbox{-}clique of $\mathcal{G}$, if for every pair of vertices in $\mathcal{X}$, there must exist at least $\gamma$ links in each $\Delta$ duration within the time interval

更新日期：2020-07-10
• arXiv.cs.SI Pub Date : 2020-07-08
Fabian Braesemann; Fabian Stephany

Thanks to the availability of large online data sets, it has become possible to quantify success in different fields of human endeavour. The study presented here contributes to this literature in evaluating the effect of social media activity, as a means of 'self-branding', to increase the chances of models being elected for the Playboy Magazine's Playmate of the Year award. We hypothesise that candidates

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-07
Hashim Abu-gellban

Online traffic news web sites do not always announce traffic events in areas in real-time. There is a capability to employ text mining and machine learning techniques on the twitter stream to perform event detection, in order to develop a real-time traffic detection system. In this present survey paper, we will deliberate the current state-of-art techniques in detecting traffic events in real-time

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-08
Abdullah KhanforSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Raby HamadiSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Hakim GhazzaiSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Ye YangSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Mohammad R. HaiderUniversity

The heterogeneity of the Internet-of-things (IoT) network can be exploited as a dynamic computational resource environment for many devices lacking computational capabilities. A smart mechanism for allocating edge and mobile computers to match the need of devices requesting external computational resources is developed. In this paper, we employ the concept of Social IoT and machine learning to downgrade

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-08
Abdullah KhanforSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Amal NammouchiSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Hakim GhazzaiSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Ye YangSchool of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA; Mohammad R. HaiderUniversity

In this paper, we propose a machine learning process for clustering large-scale social Internet-of-things (SIoT) devices into several groups of related devices sharing strong relations. To this end, we generate undirected weighted graphs based on the historical dataset of IoT devices and their social relations. Using the adjacency matrices of these graphs and the IoT devices' features, we embed the

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-07
Ikechukwu Onyenwe; Samuel Nwagbo; Njideka Mbeledogu; Ebele Onyedinma

This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a natural language processing technique called sentiment analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017, Anambra State gubernatorial election. These are Twitter discussions on the

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-08
Hamed Amini; Andreea Minca

We study a SIRD epidemic process among a heterogeneous population that interacts through a network. We give general upper bounds for the size of the epidemic starting from a (small) set of initially infected individuals. Moreover, we characterize the epidemic reproduction numbers in terms of the spectral properties of a relevant matrix based on the network adjacency matrix and the infection rates.

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-07
Daniel Vial; Sanjay Shakkottai; R. Srikant

There has been recent interest in collaborative multi-agent bandits, where groups of agents share recommendations to decrease per-agent regret. However, these works assume that each agent always recommends their individual best-arm estimates to other agents, which is unrealistic in envisioned applications (machine faults in distributed computing or spam in social recommendation systems). Hence, we

更新日期：2020-07-09
• arXiv.cs.SI Pub Date : 2020-07-07
Bruno Messias F. de Resende; Luciano da F. Costa

In this paper we investigated the possibility to use the magnetic Laplacian to characterize directed graphs (a.k.a. networks). Many interesting results are obtained, including the finding that community structure is related to rotational symmetry in the spectral measurements for a type of stochastic block model. Due the hermiticity property of the magnetic Laplacian we show here how to scale our approach

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Liz McQuillan; Erin McAweeney; Alicia Bargar; Alex Ruch

How can the birth and evolution of ideas and communities in a network be studied over time? We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic. We use network mapping to detect accounts creating content surrounding COVID-19, then Latent Dirichlet Allocation to extract topics, and

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Yi Han; Shanika Karunasekera; Christopher Leckie

Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society as a whole, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Rasoul Shafipour; Gonzalo Mateos

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-06

Balanced graph partitioning is a critical step for many large-scale distributed computations with relational data. As graph datasets have grown in size and density, a range of highly-scalable balanced partitioning algorithms have appeared to meet varied demands across different domains. As the starting point for the present work, we observe that two recently introduced families of iterative partitioners---those

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Aditya Pal; Chantat Eksombatchai; Yitong Zhou; Bo Zhao; Charles Rosenberg; Jure Leskovec

Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-01
Katherine McLeod; Petros Spachos; Konstantinos Plataniotis

Mental health and general wellness are becoming a growing concern in our society. Environmental factors contribute to mental illness and have the power to affect a person's wellness. This work presents a smartphone-based wellness assessment system and examines if there is any correlation with one's environment and their wellness. The introduced system was initiated in response to a growing need for

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-04
Pavan Yedavalli; Krishna Kumar; Paul Waddell

Abrupt changes in the environment, such as increasingly frequent and intense weather events due to climate change or the extreme disruption caused by the coronavirus pandemic, have triggered massive and precipitous human mobility changes. The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-06-23
Stefano Cresci

On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Zheng WangDepartment of Computer Science, University of Science and Technology Beijing; Xiaojun YeSchool of Software, Tsinghua University; Chaokun WangSchool of Software, Tsinghua University; Jian CuiDepartment of Computer Science, University of Science and Technology Beijing; Philip S. YuDepartment of Computer Science, University of Illinois at Chicago

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research. Semi-supervised network embedding takes advantage of labeled data, and has shown promising performance. However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. To

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-07
Paheli Bhattacharya; Kripabandhu Ghosh; Arindam Pal; Saptarshi Ghosh

Computing similarity between two legal case documents is an important and challenging task in Legal IR, for which text-based and network-based measures have been proposed in literature. All prior network-based similarity methods considered a precedent citation network among case documents only (PCNet). However, this approach misses an important source of legal knowledge -- the hierarchy of legal statutes

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-06
Amol Kapoor; Xue Ben; Luyang Liu; Bryan Perozzi; Matt Barnes; Martin Blais; Shawn O'Banion

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and

更新日期：2020-07-08
• arXiv.cs.SI Pub Date : 2020-07-06
Di Jin; Zhizhi Yu; Dongxiao He; Carl Yang; Philip S. Yu; Jiawei Han

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research attention. Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors. However, this complicated

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-06
J. Fumanal-Idocin; A. Alonso-Betanzos; O. Cordón; H. Bustince; M. Minárová

In the present article we study social network modelling using human interaction as a basis. To do so, we propose a new set of functions, Affinities, designed to capture the nature of the local interactions among each pair of actors in a network. Using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-05
Giuliano Cornacchia; Giulio Rossetti; Luca Pappalardo

Modelling human mobility is crucial in several areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. On the other hand, those models that capture some

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-05
Jianlong Zhou; Hamad Zogan; Shuiqiao Yang; Shoaib Jameel; Guandong Xu; Fang Chen

The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. For instance, depression is one of the most common mental health issues according

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-04
Kaylea Champion

What factors influence the decision to vandalize? Although the harm is clear, the benefit to the vandal is less clear. In many cases, the thing being damaged may itself be something the vandal uses or enjoys. Vandalism holds communicative value: perhaps to the vandal themselves, to some audience at whom the vandalism is aimed, and to the general public. Viewing vandals as rational community participants

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-04
Zhuoyue Xiao; Yutao Zhang; Bo Chen; Xiaozhao Liu; Jie Tang

We present a manually-labeled Author Name Disambiguation(AND) Dataset called WhoisWho, which consists of 399,255 documents and 45,187 distinct authors with 421 ambiguous author names. To label such a great amount of AND data of high accuracy, we propose a novel annotation framework where the human and computer collaborate efficiently and precisely. Within the framework, we also propose an inductive

更新日期：2020-07-07
• arXiv.cs.SI Pub Date : 2020-07-04
Weiyi Zhou; Minha Lee; Qianqian Sun; Weiyu Luo; Chenfeng Xiong; Lei Zhang

The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states

更新日期：2020-07-07
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