Autoregressive graph Volterra models and applications

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing...

Temporal Network Prediction and Interpretation

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods...

Influence of clustering coefficient on network embedding in link prediction

Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we...

Predicting Near-Future Demand of Self-Storage Rooms

The value of data has increased enormously over the last couple of years. Many datasets contain valuable information that can, for example, be used to make forecasts. In this thesis, the dataset of a company in the self-storage industry is analyzed. The company offers customers rental storage facilities, such as lockers, rooms, containers at...

Information Diffusion on Temporal Networks

As an important carrier of information diffusion, social media has experienced a huge increase in the number of users and also has a big effect on the way of how information diffuses. For example, Facebook and Youtube have attracted more than 1.6 and 1.3 billion users until 2020, respectively. The use of internet and online social network have...

Self-Driven Graph Volterra Models for Higher-Order Link Prediction

Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict...

Susceptible-infected-spreading-based network embedding in static and temporal networks

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task....

Diffusion based temporal network embedding for link prediction

Link prediction in complex networks has attracted increasing attention. The link prediction algorithms can be used to retrieve missing information, identify spurious interactions, capturing net- work evolution, and so on. Recently, network embedding has been proposed as a new strategy to embed network into low-dimensional vector space. By...

Quantum network routing via link prediction

A network of devices capable of transmitting quantum information called a quantum network has promising applications with vast benefits. One of the most near term achievable application is the quantum key distribution. It could be used for sharing a secret key between two end-users through any insecure authenticated communication channel. Other...

Link Prediction using Temporal Information in Multilayer Networks

There is an increasing attention towards link prediction in complex networks both in physical and computer science communities. Particularly Online Social Networks (OSNs) are<br/>becoming the most popular platforms for information sharing, content creation and communication between users on the Internet. However, most of the research was done...