Alzheimer’s disease (AD) is characterized by complex alterations in brain connectivity, making the understanding of these patterns critical for early diagnosis and intervention. This study presents a motif-based analysis of functional brain connectivity utilizing synthetic adjacency matrices of virtual connectomics derived from Alzheimer’s Disease Neuroimaging Initiative (ADNI) data. Our approach involves first sparsifying the functional brain network to enhance the significance of the connections, followed by the construction of a directed network from bidirectional correlation-based edge weights between ROIs (Regions of Interest) to accurately capture the flow of information. By systematically searching for recurrent motifs within these networks, we aim to identify specific patterns of connectivity that may distinguish AD patients from healthy controls. Motifs are fundamental building blocks that provide insights into the functional organization of the brain and can reveal underlying mechanisms of neurodegeneration. This analysis not only contributes to the characterization of brain network alterations in AD but also demonstrates the utility of synthetic connectomes in neuroimaging research. Our findings have implications for developing network-based biomarkers and enhancing our understanding of the pathophysiology of Alzheimer’s disease, highlighting the importance of integrating advanced network analysis techniques to unravel the complexities of brain connectivity in neurodegenerative conditions.

In this paper, we focus on network robustness in complex networks by identifying those links within a network graph G whose attack/removal would cause a severe network damage. More specifically, we investigate the role of the effective resistance matrix in identifying an order of links more vulnerable to attacks. In our previous works, we have both evaluated a strategy of link removals based on the ranking provided by the Hadamard product matrix between the adjacency matrix of G and the effective resistance matrix, and a strategy of node removals based on the ranking of the diagonal elements of the pseudoinverse of the Laplacian matrix associated to G. Now, through a real-world networking scenario of an Internet backbone, we start considering the line graph L(G) of G (i.e. the dual graph in which the links of G are nodes). Then, we investigate if removing nodes in the line graph is the same as removing links in G. The relation between the Laplacian of the line graph and the graph itself is not obvious, which does not allow us to immediately map the performance of a node removal strategy to the performance of a link removal strategy. Carrying out our analysis on Erdős-Rényi, Watts-Strogatz and Bárabasi-Albert networks, we look for a relation, if existing, between (a) the node removal in the line graph of G and (b) the link removal in G. Results show that the two attack strategies show a notable degree of similarity mostly on the Bárabasi-Albert networks.

Methods for detecting the community structure in complex networks have mainly focused on network topology, neglecting the rich content information often associated with nodes. In the last few years, the compositional dimension contained in many real-world networks has been recognized fundamental to find network divisions which better reflect group organization. In this paper, we propose a multiobjective genetic framework which integrates the topological and compositional dimensions to uncover community structure in attributed networks. The approach allows to experiment different structural measures to search for densely connected communities, and similarity measures between attributes to obtain high intracommunity feature homogeneity. An efficient and efficacious post-processing local merge procedure enables the generation of high quality solutions, as confirmed by the experimental results on both synthetic and real-world networks, and the comparison with several state-of-the-art methods.

When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account when describing networking activities: in this paper, we first evaluate the effects of node selfishness in opportunistic networks. Then, we propose a routing mechanism for managing node selfishness in opportunistic communications, namely, SORSI (Social-based Opportunistic Routing with Selfishness detection and Incentive mechanisms). SORSI exploits the social-based nature of node mobility and other social features of nodes to optimize message dissemination together with a selfishness detection mechanism, aiming at discouraging selfish behaviors and boosting data forwarding. Simulating several percentages of selfish nodes, our results on real-world mobility traces show that SORSI is able to outperform the social-based schemes Bubble Rap and SPRINT-SELF, employing also selfishness management in terms of message delivery ratio, overhead cost, and end-to-end average latency. Moreover, SORSI achieves delivery ratios and average latencies comparable to Epidemic Routing while having a significant lower overhead cost.

Contact networks have been recognized to have a central role in the dynamic behavior of spreading processes. The availability of cost-optimal curing strategies, able to control the epidemic propagation, are of primary importance for the design of efficient treatments reducing the number of infected individuals and the extinction time of the infection. In this paper, we investigate the use of Genetic Algorithms for solving the problem of finding an optimal curing strategy in a network where a virus spreads following the Susceptible-Infected-Susceptible (SIS) epidemic model. Exploiting the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS spreading process, we propose a constrained genetic algorithm which determines specific curing rates to each node composing the network, in order to minimize the total curing cost, while suppressing the epidemic. Experiments on both synthetic and real-world networks show that the approach finds solutions whose curing cost is lower than that obtained by a classical baseline method.

A method to enhance the robustness of a network, based on Genetic Algorithms, is proposed. The approach optimizes the effective graph resistance of a network, a measure of robustness derived from the field of electric circuit analysis, that can be computed as a cumulative sum of the eigenvalues of the Laplacian matrix associated with the network. Specialized variation operators allow the method to find a solution almost always coinciding with that obtained by the exhaustive search. Experiments on synthetic and real life networks show that the approach outperforms heuristic strategies extensively investigated, by giving the exact solution in a high percentage of the considered networks.

A many-objective optimization algorithm for community detection in multi-layer networks is proposed. The method exploits the modularity concept as function to be simultaneously optimized on all the network layers to uncover multi-layer communities. In addition, three different strategies to choice the best solution from the set of solutions of the Pareto front are presented. Simulations on several synthetic networks reveal that our method is able to extract high quality communities. A comparison with state-of-the-art approaches shows that the method is competitive and, in many cases, it is also able to outperform existing community detection algorithms for multi-layer networks.

In this paper, we present a forwarding algorithm for mobile social opportunistic networks which is both centrality-based and energy-aware. In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Centrality-based forwarding schemes use the structurally important nodes (i.e. nodes having many contacts with neighbors) as message relays and have been shown to be good candidates for social forwarding in opportunistic network environments. However, the most central nodes which are structurally more important than other network nodes, have the disadvantage to be used very frequently as message relays, thus consuming their energy very quickly. Here, we exploit the advantage of using a centrality-based scheme which is distributed and needs only the history of contacts as social information. In addition, we modulate the computed node centrality value using the node energy level in order to avoid transmissions in nodes more central but with a limited energy budget. Simulating three real-world mobility traces, we show that our scheme is able to achieve better delivery ratio and overhead ratio while sensibly reducing the energy consumption of nodes.

Pocket Switched Networks (PSNs) are challenged wireless networks of handheld mobile devices that exploit user mobility to distribute data in an opportunistic way. In PSNs, a fundamental issue is the choice of a suitable relay node for message forwarding. In such environments, disconnections are very frequent and an end-to-end path from source to destination usually does not exist. Consequently, routing protocols are based on a hop-by-hop communication paradigm. Most of these protocols exploit social behaviour characteristics to decide whether an encountered node is a good relay. Social network information is commonly extracted from encounters detected between mobile devices. However, Internet added online social interaction techniques, which are not based on physical meetings and that reflect users' online behaviour. In this paper we present novel routing metrics for social-based routing in PSNs that exploit both offline and online users' social network information. By proposing a model of dynamic online social network that uses information extracted from offline and online user behaviour, we show that routing centrality metrics, combining node centrality extracted from dynamic online social network and centrality extracted from detected social network, improve delivery ratio and in some scenarios even reduce end-to-end delay.

Opportunistic networks are a generalization of Delay Tolerant Networks (DTNs) where communication is challenged by frequent disconnections and encounter patterns between mobile devices are unpredictable. Opportunistic routing protocols attempt to enable the forwarding of messages via encounters between devices, while dealing with the problems of large delays and lack of end-to-end connectivity. To save energy in mobile environments, such routing protocols must minimize unnecessary message replication. This paper presents FSF (Friendlist-based Social Forwarding), an opportunistic routing scheme that exploits both social network information detected through encounters between mobile devices and pre-existing online social network information. To select an effective forwarding node, FSF measures the forwarding capability of a node when compared to an encountered node in terms of node degree cen-trality measured on the temporal encounter network and online tie strength. Simulations with experimental datasets containing encounter records with corresponding Facebook friendlists of the participants show that the proposed routing scheme allows mobile users to achieve good delivery ratio, while forwarding fewer messages than other existing algorithms.

Delay tolerant networks (DTNs) are sparse wireless networks where most of the time there does not exist a complete path from the source node to the destination node. In this context, where connectivity is intermittent, but where the possibility of communication is still desirable, conventional routing protocols are unsuitable to deliver messages between nodes. The design of efficient routing protocols is a fundamental problem in DTNs. Many different routing approaches for DTNs have been proposed in the literature, each one based on different characteristics and properties. In most cases, their performance have been evaluated considering aspects like delivery ratio and delivery latency, without considering energy consumption constraint. In this work we focus on the energy consumption issues of the routing protocols. We present a performance comparison of the Epidemic, Spray and Wait, PROPHET, MaxProp and Bubble Rap routing protocols with respect to energy consumption, evaluating how the energy consumption impacts the performance of the protocols and how the different forwarding algorithms affect the energy usage in the mobile devices.