Jo Cheriyan

Temporal networks extend complex networks by explicitly incorporating the time dimension into the analysis of entities and their interactions. With the rapid growth of large-scale social and information networks, modeling time-evolving relationships has become increasingly important, particularly for temporal link prediction. Unlike static networks, temporal link prediction must account for evolving node interactions, making it a substantially more challenging problem. This article focuses on temporal link prediction and presents a unified probabilistic framework for analyzing how representative deep learning approaches—including snapshot-based, temporal attention-based, and continuous-time methods—model temporal interactions. Rather than treating these methods as independent solutions, the proposed framework clarifies the underlying modeling assumptions that connect discrete- and continuous-time representations, interaction intensity estimation, time-to-event prediction, and anomaly detection. Extensive experiments on real-world temporal networks demonstrate that modeling choices consistent with the unified modeling lead to more robust and interpretable performance across different temporal link prediction subproblems, particularly for interaction intensity estimation, time-to-event prediction, and anomaly detection. The proposed framework provides a principled basis for understanding and comparing temporal network representations and learning objectives in practice.

Complex networks represent non-trivial topologies of connected natural and artificial systems. Revealing how information spreads within such multilayered systems is critical to the investigation of processes such as social influence, rumor spread, and epidemic spread. This article presents a clustering method based on a centrality-based measure of a multilayer personalized PageRank (m-PageRank) that aggregates node influence across layers to identify structurally and functionally cohesive clusters. The suggested framework supports realistic modeling of intra- and inter-cluster information propagation in complex networks. In addition, we examine the effects of clustering properties on diffusion efficiency and the epidemic threshold. Experimental verification shows that the m-PageRank-based clustering improves diffusion insight by effectively detecting influential nodes and tight communities, thus uncovering how enhanced clustering conditions the collective spread dynamics in multilayer networks.

Research in complex network analysis, including tasks such as information dissemination and link prediction, has traditionally advanced by examining groups of nodes with similar characteristics. In multilayer complex networks, these analyses are performed layer-wise, with each layer representing a distinct feature. However, this approach often neglects interlayer interactions, leading to limited predictive power, underestimation of network resilience, and incomplete identification of influential nodes. This article introduces a novel block modeling technique that integrates dominant features from multiple layers, addressing these shortcomings. Our approach introduces a new centrality metric that combines layer weight and PageRank centrality to identify influential nodes within multilayer networks. Nodes are aggregated into blocks centered around these influencers, accounting for both intra- and interlayer connections. Empirical evaluation of various datasets demonstrates that our method effectively identifies influential nodes, thereby enhancing information dissemination and link prediction across diverse multilayer network structures. This technique offers significant potential for improving decision-making processes in various fields, including social network analysis, transportation systems, and biological networks.

Diffusion dynamics is the transfer of information from one node to another. Information diffusion has two goals: maximize or minimize information spread over the network. Attractive information such as innovations, awareness campaigns, branding, and advertising help people positively. However, awful information such as rumors, malicious viruses, pornography, and revenge disturb people. The negative information contributes to chaos; therefore, it must be blocked and inhibited from further diffusion. We are motivated to study the problem, namely Influence Minimization . The new information alters the energy level or entropy associated with a node. Entropy quantifies the influence propagation rate across the network. This article proposes two reduction policies that reduce repulsive information’s influence through entropy. We validate the proposed system by considering the user response and surveillance on real-time networks.

Complex Networks can depict a clear image of real-world systems. A real-world scenario can be represented a graph with interconnected layers - called a multi-layer network. Finding motifs can give an idea of the topology of complex systems and helps to understand the dynamics in the graphs. Looking at motifs as atoms of the network is helpful to analyze the relationship between nodes, and between layers. In this work, we suggest a sub-graph enumeration approach to find and count the motifs in multi-layer network. The proposed work has many application in graph mining, particularly to structure and dynamics of complex networks.