5,595 research outputs found
Predicting Item Popularity: Analysing Local Clustering Behaviour of Users
Predicting the popularity of items in rating networks is an interesting but
challenging problem. This is especially so when an item has first appeared and
has received very few ratings. In this paper, we propose a novel approach to
predicting the future popularity of new items in rating networks, defining a
new bipartite clustering coefficient to predict the popularity of movies and
stories in the MovieLens and Digg networks respectively. We show that the
clustering behaviour of the first user who rates a new item gives insight into
the future popularity of that item. Our method predicts, with a success rate of
over 65% for the MovieLens network and over 50% for the Digg network, the
future popularity of an item. This is a major improvement on current results.Comment: 25 pages, 11 figure
Identifying influencers in a social network : the value of real referral data
Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers
Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach
We introduce a new method to efficiently approximate the number of infections
resulting from a given initially-infected node in a network of susceptible
individuals. Our approach is based on counting the number of possible infection
walks of various lengths to each other node in the network. We analytically
study the properties of our method, in particular demonstrating different forms
for SIS and SIR disease spreading (e.g. under the SIR model our method counts
self-avoiding walks). In comparison to existing methods to infer the spreading
efficiency of different nodes in the network (based on degree, k-shell
decomposition analysis and different centrality measures), our method directly
considers the spreading process and, as such, is unique in providing estimation
of actual numbers of infections. Crucially, in simulating infections on various
real-world networks with the SIR model, we show that our walks-based method
improves the inference of effectiveness of nodes over a wide range of infection
rates compared to existing methods. We also analyse the trade-off between
estimate accuracy and computational cost, showing that the better accuracy here
can still be obtained at a comparable computational cost to other methods.Comment: 6 page
AUGUR: Forecasting the Emergence of New Research Topics
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall
The use of multilayer network analysis in animal behaviour
Network analysis has driven key developments in research on animal behaviour
by providing quantitative methods to study the social structures of animal
groups and populations. A recent formalism, known as \emph{multilayer network
analysis}, has advanced the study of multifaceted networked systems in many
disciplines. It offers novel ways to study and quantify animal behaviour as
connected 'layers' of interactions. In this article, we review common questions
in animal behaviour that can be studied using a multilayer approach, and we
link these questions to specific analyses. We outline the types of behavioural
data and questions that may be suitable to study using multilayer network
analysis. We detail several multilayer methods, which can provide new insights
into questions about animal sociality at individual, group, population, and
evolutionary levels of organisation. We give examples for how to implement
multilayer methods to demonstrate how taking a multilayer approach can alter
inferences about social structure and the positions of individuals within such
a structure. Finally, we discuss caveats to undertaking multilayer network
analysis in the study of animal social networks, and we call attention to
methodological challenges for the application of these approaches. Our aim is
to instigate the study of new questions about animal sociality using the new
toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl
A Network Science perspective of Graph Convolutional Networks: A survey
The mining and exploitation of graph structural information have been the
focal points in the study of complex networks. Traditional structural measures
in Network Science focus on the analysis and modelling of complex networks from
the perspective of network structure, such as the centrality measures, the
clustering coefficient, and motifs and graphlets, and they have become basic
tools for studying and understanding graphs. In comparison, graph neural
networks, especially graph convolutional networks (GCNs), are particularly
effective at integrating node features into graph structures via neighbourhood
aggregation and message passing, and have been shown to significantly improve
the performances in a variety of learning tasks. These two classes of methods
are, however, typically treated separately with limited references to each
other. In this work, aiming to establish relationships between them, we provide
a network science perspective of GCNs. Our novel taxonomy classifies GCNs from
three structural information angles, i.e., the layer-wise message aggregation
scope, the message content, and the overall learning scope. Moreover, as a
prerequisite for reviewing GCNs via a network science perspective, we also
summarise traditional structural measures and propose a new taxonomy for them.
Finally and most importantly, we draw connections between traditional
structural approaches and graph convolutional networks, and discuss potential
directions for future research
Disconnection of network hubs and cognitive impairment after traumatic brain injury.
Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury
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