scholarly journals Approximating the Temporal Neighbourhood Function of Large Temporal Graphs

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 211 ◽  
Author(s):  
Pierluigi Crescenzi ◽  
Clémence Magnien ◽  
Andrea Marino
Keyword(s):  
Public Transportation ◽  
Arrival Time ◽  
Time Interval ◽  
Temporal Network ◽  
Temporal Networks ◽  
Starting Time ◽  
Neighborhood Function ◽  
Temporal Graphs ◽  
Temporal Path

Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.

A Method for Controlling Arrival Time to Prevent Late Arrival by Manipulating Vehicle Timetable Information

2020 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Kyosuke Futami ◽  
Tsutomu Terada ◽  
Masahiko Tsukamoto
Keyword(s):  
Public Transportation ◽  
Psychological Factors ◽  
Arrival Time ◽  
Time Interval ◽  
Prototype System ◽  
Time Intervals ◽  
Target Vehicle ◽  
Late Arrival ◽  
Departure Time ◽  
The Relationship

Although it is socially and ethically important not to be late for a specified arrival time, late arrivals sometimes happen to people using public transportation. Although many methods aim to smooth a user's movement by providing useful information, there are few approaches to prevent late arrivals due to psychological factors. In this research, to make a user's arrival time earlier and thus prevent late arrival, we propose a method that manipulates time allowance by presenting information based on a psychological and cognitive tendency. We apply this method to a vehicle timetable system for the purpose of preventing public transit users from arriving after a target vehicle's departure time. Our proposed timetable system manipulates the time intervals between a user's target vehicle and other vehicles by introducing fictional elements such as hidden vehicles and inserted fictional vehicles. This method uses the relationship between the time allowance and the departure time interval, and it can make a user desire and accept arriving at a station earlier. We implemented a prototype system and conducted four experiments. The evaluation results confirmed that our proposed method is effective for changing a user's time allowance and actual arrival time.

scholarly journals On Verifying and Maintaining Connectivity of Interval Temporal Networks

2019 ◽  
Vol 29 (02) ◽  
pp. 1950009 ◽  
Author(s):  
Eleni C. Akrida ◽  
Paul G. Spirakis
Keyword(s):  
Polynomial Time ◽  
High Speed ◽  
Information Dissemination ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Time Interval ◽  
Temporal Network ◽  
Temporal Networks ◽  
Tree Graphs ◽  
Time Period

An interval temporal network is, informally speaking, a network whose links change with time. The term interval means that a link may exist for one or more time intervals, called availability intervals of the link, after which it does not exist (until, maybe, a further moment in time when it starts being available again). In this model, we consider continuous time and high-speed (instantaneous) information dissemination. An interval temporal network is connected during a period of time [Formula: see text], if it is connected for all time instances [Formula: see text] (instantaneous connectivity). In this work, we study instantaneous connectivity issues of interval temporal networks. We provide a polynomial-time algorithm that answers if a given interval temporal network is connected during a time period. If the network is not connected throughout the given time period, then we also give a polynomial-time algorithm that returns large components of the network that remain connected and remain large during [Formula: see text]; the algorithm also considers the components of the network that start as large at time [Formula: see text] but dis-connect into small components within the time interval [Formula: see text], and answers how long after time [Formula: see text] these components stay connected and large. Finally, we examine a case of interval temporal networks on tree graphs where the lifetimes of links and, thus, the failures in the connectivity of the network are not controlled by us; however, we can “feed” the network with extra edges that may re-connect it into a tree when a failure happens, so that its connectivity is maintained during a time period. We show that we can with high probability maintain the connectivity of the network for a long time period by making these extra edges available for re-connection using a randomized approach. Our approach also saves some cost in the design of availabilities of the edges; here, the cost is the sum, over all extra edges, of the length of their availability-to-reconnect interval.

Reconstructing Diffusion Model for Virality Detection in News Spread Networks

2021 ◽  
pp. 98-111
Author(s):  
Kritika Jain ◽  
Ankit Garg ◽  
Somya Jain
Keyword(s):  
Diffusion Model ◽  
Experimental Analysis ◽  
Recommendation System ◽  
Continuous Process ◽  
The Internet ◽  
Temporal Network ◽  
Temporal Networks ◽  
Spreading Pattern ◽  
Effective Product ◽  
Business Technology

In today's competitive world, organizations take advantage of widely-available data to promote their products and increase their revenue. This is achieved by identifying the reader's preference for news genre and patterns in news spread network. Spreading news over the internet seems to be a continuous process which eventually triggers the evolution of temporal networks. This temporal network comprises of nodes and edges, where node corresponds to published articles and similar articles are connected via edges. The main focus of this article is to reconstruct a susceptible-infected (SI) diffusion model to discover the spreading pattern of news articles for virality detection. For experimental analysis, a dataset of news articles from four domains (business, technology, entertainment, and health) is considered and the articles' rate of diffusion is inferred and compared. This will help to build a recommendation system, i.e. recommending a particular domain for advertisement and marketing. Hence, it will assist to build strategies for effective product endorsement for sustainable profitability.

Characteristics Analysis of the Photons Arrival Time Interval of Laser Beat Signal

2012 ◽  
Vol 32 (4) ◽  
pp. 0403001 ◽  
Author(s):  
刘立生 Liu Lisheng ◽  
张合勇 Zhang Heyong ◽  
赵帅 Zhao Shuai ◽  
郭劲 Guo Jin
Keyword(s):  
Arrival Time ◽  
Beat Signal ◽  
Time Interval ◽  
Characteristics Analysis

scholarly journals Temporal network alignment via GoT-WAVE

2019 ◽  
Vol 35 (18) ◽  
pp. 3527-3529 ◽  
Author(s):  
David Aparício ◽  
Pedro Ribeiro ◽  
Tijana Milenković ◽  
Fernando Silva
Keyword(s):  
User Interface ◽  
State Of The Art ◽  
Source Code ◽  
Network Alignment ◽  
Supplementary Information ◽  
Temporal Network ◽  
Temporal Networks ◽  
Supplementary Data ◽  
Node Similarity ◽  
User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The Effect Of Determinant Factors In The Family On The Arrival Time Of Patients Stroke In Emergency Installation

2020 ◽  
Vol 9 (1) ◽  
pp. 44-53
Author(s):  
Muhammad Ikhwan Kosasih ◽  
Nugrahaeni Firdausi ◽  
Erwin Yektiningsih ◽  
Zauhani Kusnul
Keyword(s):  
Significant Relationship ◽  
Arrival Time ◽  
Decision Makers ◽  
Time Interval ◽  
Stroke Patients ◽  
The Family ◽  
Result Show ◽  
Important Health ◽  
Level Of Knowledge ◽  
The Right

Stroke is an important health problem. The speed with which a stroke sufferer gets the right treatment cause a big influence on stroke management. This study analyze the influence of various factors in the family on the arrival time of stroke patients in the emergency department of the Kediri district hospital. The study was conducted during May-July 2019 and found stroke patients as many as 88. The result show that educational factors have a significant relationship with the level of knowledge, and  the family age, job, people who knew the stroke and decision-makers in the family have a significant relationship with the time interval between the stroke attack with the arrival of patients in the emergency room. From this study, it can be concluded that family factors play an important role in the time interval between a stroke and the arrival of a patient on IGD to get proper treatment.

scholarly journals EVENT GRAPHS: ADVANCES AND APPLICATIONS OF SECOND-ORDER TIME-UNFOLDED TEMPORAL NETWORK MODELS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950006
Author(s):  
ANDREW MELLOR
Keyword(s):  
Protein Interactions ◽  
Temporal Structure ◽  
Network Models ◽  
Higher Order ◽  
Temporal Network ◽  
Temporal Networks ◽  
Protein Protein Interactions ◽  
Temporal Features ◽  
Aggregated Model ◽  
Temporal Interaction

Recent advances in data collection and storage have allowed both researchers and industry alike to collect data in real time. Much of this data comes in the form of ‘events’, or timestamped interactions, such as email and social media posts, website clickstreams, or protein–protein interactions. This type of data poses new challenges for modeling, especially if we wish to preserve all temporal features and structure. We highlight several recent approaches in modeling higher-order temporal interaction and bring them together under the umbrella of event graphs. Through examples, we demonstrate how event graphs can be used to understand the higher-order topological-temporal structure of temporal networks and capture properties of the network that are unobservable when considering either a static (or time-aggregated) model. We introduce new algorithms for temporal motif enumeration and provide a novel analysis of the communicability centrality for temporal networks. Furthermore, we show that by modeling a temporal network as an event graph our analysis extends easily to non-dyadic interactions, known as hyper-events.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

On querying historical k-cores

2021 ◽  
Vol 14 (11) ◽  
pp. 2033-2045
Author(s):  
Michael Yu ◽  
Dong Wen ◽  
Lu Qin ◽  
Ying Zhang ◽  
Wenjie Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Time Window ◽  
Simple Graph ◽  
Time Interval ◽  
Research Attention ◽  
High Effectiveness ◽  
Running Time ◽  
Temporal Graphs ◽  
Index Size ◽  
Novel Algorithm

Many real-world relationships between entities can be modeled as temporal graphs, where each edge is associated with a timestamp or a time interval representing its occurrence. K -core is a fundamental model used to capture cohesive subgraphs in a simple graph and have drawn much research attention over the last decade. Despite widespread research, none of the existing works support the efficient querying of historical k -cores in temporal graphs. In this paper, given an integer k and a time window, we study the problem of computing all k -cores in the graph snapshot over the time window. We propose an index-based solution and several pruning strategies to reduce the index size. We also design a novel algorithm to construct this index, whose running time is linear to the final index size. Lastly, we conducted extensive experiments on several real-world temporal graphs to show the high effectiveness of our index-based solution.

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