Spreading processes are ubiquitous in nature and society, from the diffusion of information in social platforms to the spread of diseases within populations. Many real-world systems can be represented as networks, where a piece of information or a disease spreads along links conn
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Spreading processes are ubiquitous in nature and society, from the diffusion of information in social platforms to the spread of diseases within populations. Many real-world systems can be represented as networks, where a piece of information or a disease spreads along links connecting nodes. Different nodes and links often differ in their network properties and play distinct roles in a spreading process. Based on network properties of nodes or links, practitioners may be interested in identifying key nodes as the seed nodes to maximally diffuse a piece of information, or removing specific links to mitigate the spreading. In this thesis, we study the roles of a node or a link in a spreading process from three different perspectives and investigate how these roles relate to the properties of nodes and links within the underlying network.
We first explore how the network properties of a node can be used to predict the spreading influence of the node, defined as the average number of nodes that are ultimately infected when this node is the only seed node. Previous studies have shown that combining node properties derived from local and global topological information can better predict nodal influence than using a single metric. In Chapter 2, we investigate whether using relatively local information is sufficient for the prediction. To address this question, we define an iterative metric set by leveraging the iterative process used to derive classical nodal centralities like eigenvector centrality. The iterative metric set progressively incorporates more global information and is used as the feature set in a regression model to predict nodal spreading influence. The iterative metric set is then used as the feature set in a regression model to predict the spreading influence of a node. We find that the model using the iterative metric set that includes relatively local information achieves comparable prediction quality with the method that includes both local and global information, in various networks.
A spreading process can be mitigated by blocking social contacts, i.e., time-specific interactions. In Chapter 3, we investigate how the network properties of a contact are associated with the mitigation effect when the contact is blocked. We develop probabilistic contact blocking strategies, which remove contacts (temporal links) based on their properties in a temporal network, to mitigate the spread of a Susceptible-Infected-Recovered spreading process. The removal probability of a contact depends on a given centrality metric of the corresponding link in the time-aggregated network and the occurring time of the contact. We propose diverse link centrality metrics, and each centrality metric leads to a unique contact blocking strategy. Our results indicate that the spread of the epidemic is most effectively mitigated when contacts between node pairs that have fewer contacts and contacts that occur earlier in time are more likely to be removed.
The role of a link in a spreading process can also be reflected by the extent to which the link is used in the process. Many real-world systems may involve interactions among groups of more than two individuals and can therefore be represented as temporal higher-order networks. Chapter 4 explores the Susceptible-Infected threshold spreading process unfolding on temporal higher-order networks with two objectives: (1) to understand the contribution of each hyperlink to the spreading process, defined as the average number of nodes that are directly infected via the activation of the hyperlink starting from an arbitrary seed node, and (2) to investigate hyperlinks with what network properties tend to contribute more to the spreading process. This understanding is crucial for developing effective strategies to mitigate a spreading process. Given a temporal higher-order network, we propose to construct a weighted higher-order network, the so-called diffusion backbone, where the weight of each hyperlink denotes its contribution to the spreading process. We then systematically design centrality metrics for hyperlinks in a temporal higher-order network, where each centrality metric captures a specific property of the hyperlink within a temporal higher-order network and is used to estimate the ranking of hyperlinks by their weights in the backbone. We find and explain why certain centrality metrics can better estimate the contributions of hyperlinks under different parameters of the spreading process.
The last chapter reflects on the insights of this thesis and discusses possible future directions related to our research.@en