74 research outputs found
Mesoscopic structure and social aspects of human mobility
The individual movements of large numbers of people are important in many
contexts, from urban planning to disease spreading. Datasets that capture human
mobility are now available and many interesting features have been discovered,
including the ultra-slow spatial growth of individual mobility. However, the
detailed substructures and spatiotemporal flows of mobility - the sets and
sequences of visited locations - have not been well studied. We show that
individual mobility is dominated by small groups of frequently visited,
dynamically close locations, forming primary "habitats" capturing typical daily
activity, along with subsidiary habitats representing additional travel. These
habitats do not correspond to typical contexts such as home or work. The
temporal evolution of mobility within habitats, which constitutes most motion,
is universal across habitats and exhibits scaling patterns both distinct from
all previous observations and unpredicted by current models. The delay to enter
subsidiary habitats is a primary factor in the spatiotemporal growth of human
travel. Interestingly, habitats correlate with non-mobility dynamics such as
communication activity, implying that habitats may influence processes such as
information spreading and revealing new connections between human mobility and
social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table
(supporting information
Patterns of Individual Shopping Behavior
Much of economic theory is built on observations of aggregate, rather than
individual, behavior. Here, we present novel findings on human shopping
patterns at the resolution of a single purchase. Our results suggest that much
of our seemingly elective activity is actually driven by simple routines. While
the interleaving of shopping events creates randomness at the small scale, on
the whole consumer behavior is largely predictable. We also examine
income-dependent differences in how people shop, and find that wealthy
individuals are more likely to bundle shopping trips. These results validate
previous work on mobility from cell phone data, while describing the
unpredictability of behavior at higher resolution.Comment: 4 pages, 5 figure
DTN routing optimised by human routines: the HURRy protocol
This paper proposes the HURRy (HUman Routines used for Routing) protocol, which infers and benefits from the social behaviour of nodes in disruptive networking environments. HURRy incorporates the contact duration to the information retrieved from historical encounters among neighbours, so that smarter routing decisions can be made. The specification of HURRy is based on the outcomes of a thorough experiment, which highlighted the importance of distinguishing between short and long contacts and deriving mathematical relations in order to optimally prioritize the available routes to a destination. HURRy introduces a novel and more meaningful rating system to evaluate the quality of each contact and overcome the limitations of other routing approaches in social environments.European Commission, Horizon 2020, Grant Agreement number 645124, UMOBIL
Tiles: an online algorithm for community discovery in dynamic social networks
Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify
Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities
Communities in networks are commonly considered as highly cohesive subgraphs
which are well separated from the rest of the network. However, cohesion and
separation often cannot be maximized at the same time, which is why a
compromise is sought by some methods. When a compromise is not suitable for the
problem to be solved it might be advantageous to separate the two criteria. In
this paper, we explore such an approach by defining communities as well
separated subgraphs which can have one or more cohesive cores surrounded by
peripheries. We apply this idea to link communities and present an algorithm
for constructing hierarchical core-periphery structures in link communities and
first test results.Comment: 12 pages, 2 figures, submitted version of a paper accepted for the
7th International Conference on Complex Networks and Their Applications,
December 11-13, 2018, Cambridge, UK; revised version at
http://141.20.126.227/~qm/papers
Spatiotemporal correlations of handset-based service usages
We study spatiotemporal correlations and temporal diversities of
handset-based service usages by analyzing a dataset that includes detailed
information about locations and service usages of 124 users over 16 months. By
constructing the spatiotemporal trajectories of the users we detect several
meaningful places or contexts for each one of them and show how the context
affects the service usage patterns. We find that temporal patterns of service
usages are bound to the typical weekly cycles of humans, yet they show maximal
activities at different times. We first discuss their temporal correlations and
then investigate the time-ordering behavior of communication services like
calls being followed by the non-communication services like applications. We
also find that the behavioral overlap network based on the clustering of
temporal patterns is comparable to the communication network of users. Our
approach provides a useful framework for handset-based data analysis and helps
us to understand the complexities of information and communications technology
enabled human behavior.Comment: 11 pages, 15 figure
Towards Online Multiresolution Community Detection in Large-Scale Networks
The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks
Evolving network structure of academic institutions
Today’s colleges and universities consist of highly complex structures that dictate interactions between the administration, faculty, and student body. These structures can play a role in dictating the efficiency of policy enacted by the administration and determine the effect that curriculum changes in one department have on other departments. Despite the fact that the features of these complex structures have a strong impact on the institutions, they remain by-and-large unknown in many cases. In this paper we study the academic structure of our home institution of Trinity College in Hartford, CT using the major and minor patterns between graduating students to build a temporal multiplex network describing the interactions between different departments. Using recent network science techniques developed for such temporal networks we identify the evolving community structures that organize departments’ interactions, as well as quantify the interdisciplinary centrality of each department. We implement this framework for Trinity College, finding practical insights and applications, but also present it as a general framework for colleges and universities to better understand their own structural makeup in order to better inform academic and administrative policy
Evidence for a Conserved Quantity in Human Mobility
Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
Background: Network communities help the functional organization and
evolution of complex networks. However, the development of a method, which is
both fast and accurate, provides modular overlaps and partitions of a
heterogeneous network, has proven to be rather difficult. Methodology/Principal
Findings: Here we introduce the novel concept of ModuLand, an integrative
method family determining overlapping network modules as hills of an influence
function-based, centrality-type community landscape, and including several
widely used modularization methods as special cases. As various adaptations of
the method family, we developed several algorithms, which provide an efficient
analysis of weighted and directed networks, and (1) determine pervasively
overlapping modules with high resolution; (2) uncover a detailed hierarchical
network structure allowing an efficient, zoom-in analysis of large networks;
(3) allow the determination of key network nodes and (4) help to predict
network dynamics. Conclusions/Significance: The concept opens a wide range of
possibilities to develop new approaches and applications including network
routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information
containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18
module definitions, 129 different modularization methods, 13 module
comparision methods) and 396 references. All algorithms can be downloaded
from this web-site: http://www.linkgroup.hu/modules.ph
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