We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.

/pdf/consistency-and-differences-between-centrality-measures-1ej3rjtb7d.pdf

Consistency and differences between centrality measures across distinct classes of networks.

Agent-based Internet of Things: State-of-the-art and research challenges

Background
Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?

/pdf/towards-a-methodology-for-validation-of-centrality-measures-4gwrf79p55.pdf

Towards a methodology for validation of centrality measures in complex networks.

The students and administrative staff members of Punjab University, Lahore (new campus) were screened for the presence of hepatitis B antigens (HbsAg) and HCV antibodies (anti- HCV). The prevalence rate of anti-HCV was found 1.48 % and HbsAg was 2.46 % respectively, no overlapping between the seropositiity of HBV and HCV. In order to prevent the transmission of HH H HBV and HCV through blood transfusion, it is essential that all donors should be screened for anti- HCV and HbsAg. There is need to create the awareness about it.

Prevalence of hepatitis b and c in university of the punjab, quaid-e-azam campus, lahore

The Internet of Things vision has recently emerged as a result of a proliferation of a large number of networked consumer electronic devices. There is a growing need to autonomously monitor power consumption of these devices. We present a self-organizing distributed algorithm for the dynamic approximation of power consumption in networked consumer electronic devices.

Self-organized power consumption approximation in the Internet of Things

Wireless Sensor Networks (WSN) are a fast-emerging area of interest in the domain of large-scale communication networks. However, design of novel WSN applications requires first developing models and performing extensive simulation. Modern large-scale WSNs are expected to be both dynamic as well as random and often spread over a large scale. However, this problem has not previously been addressed in literature. In our previous works, we have demonstrated how Agent-based modeling may be used to effectively model various types of Complex Adaptive Systems such as, but not limited to, Self-organizing Communication infrastructures. In this paper, we present first steps towards providing a comprehensive set of guidelines and tutorial for developing deployment models of WSNs besides modeling routing algorithms using agent-based modeling. Our simulation results demonstrate the effectiveness and ease of use coupled with a short learning curve involved in developing agent-based models of complex WSN applications.

Towards modeling complex wireless sensor networks using agents and networks: A systematic approach

The risk of malware has increased drastically in recent years due to advances in the IT industry but it also increased the need for malware analysis and prevention. Hackers inject malicious code using awful applications. In this research, a framework is proposed to identify malicious Android applications based on repacked malicious code. The sensitive features of android applications are extracted using source code. These extracted features are compared with existing malware signatures to identify repacked malicious android applications. Experiments are performed using 3490 android-based malware samples belonging to 21 different malware families. A threshold value for malware categorization is defined using fuzzy logic. If the fuzzy comparison match is greater than 40%, the application is malicious. Meanwhile, if the match is greater than 10% and less than 40%, the application is suspicious otherwise benign. Furthermore, the proposed framework presents around 74% of the repacked malware compared to other similar approaches.

Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code

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