scholarly journals Nonlinear growth: an origin of hub organization in complex networks

2017 ◽  
Vol 4 (3) ◽  
pp. 160691 ◽  
Author(s):  
Roman Bauer ◽  
Marcus Kaiser
Keyword(s):  
Real World ◽  
Preferential Attachment ◽  
Fibre Tract ◽  
Nonlinear Network ◽  
Network Function ◽  
Nonlinear Growth ◽  
Rich Club ◽  
Spreading Dynamics ◽  
Classical Models ◽  
Generic Representation

Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein–protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication–divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.

What Influences Relationship Formation in a Global Civil Society Network? An Examination of Valued Multiplex Relations

2021 ◽  
pp. 009365022110161
Author(s):  
Adam J. Saffer ◽  
Andrew Pilny ◽  
Erich J. Sommerfeldt
Keyword(s):  
Communication Network ◽  
Civil Society ◽  
Real World ◽  
Network Formation ◽  
Preferential Attachment ◽  
Interorganizational Networks ◽  
Communication Research ◽  
Relationship Formation ◽  
Interorganizational Communication ◽  
Strength Of Ties

Recent interorganizational communication research has taken up the question: why are networks structured the way they are? This line of inquiry has advanced communication network research by helping explain how and why networks take on certain structures or why certain organizations become positioned advantageously (or not). Yet, those studies assume relationships among organizations are either present or absent. This study considers how the strength of ties and multiplex relationships among organizations may reveal a more complex explanation for why networks take on certain structures. Our results challenge some long held assumptions about the mechanisms that influence network formation. For instance, our results offer important insights into the consequences of closure mechanisms, the applicability of preferential attachment to real-world networks, and the nuances of homophily in network formation on multidimensional relationships in a communication network. Implications for interorganizational networks are discussed.

scholarly journals Friendship paradox in growth networks: analytical and empirical analysis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sergei P. Sidorov ◽  
Sergei V. Mironov ◽  
Alexey A. Grigoriev
Keyword(s):  
Real World ◽  
Preferential Attachment ◽  
Empirical Studies ◽  
Mean Field ◽  
Network Models ◽  
Average Degree ◽  
Closure Model ◽  
Triadic Closure ◽  
Attachment Mechanism ◽  
Over Time

AbstractMany empirical studies have shown that in social, citation, collaboration, and other types of networks in real world, the degree of almost every node is less than the average degree of its neighbors. This imbalance is well known in sociology as the friendship paradox and states that your friends are more popular than you on average. If we introduce a value equal to the ratio of the average degree of the neighbors for a certain node to the degree of this node (which is called the ‘friendship index’, FI), then the FI value of more than 1 for most nodes indicates the presence of the friendship paradox in the network. In this paper, we study the behavior of the FI over time for networks generated by growth network models. We will focus our analysis on two models based on the use of the preferential attachment mechanism: the Barabási–Albert model and the triadic closure model. Using the mean-field approach, we obtain differential equations describing the dynamics of changes in the FI over time, and accordingly, after obtaining their solutions, we find the expected values of this index over iterations. The results show that the values of FI are decreasing over time for all nodes in both models. However, for networks constructed in accordance with the triadic closure model, this decrease occurs at a much slower rate than for the Barabási–Albert graphs. In addition, we analyze several real-world networks and show that their FI distributions follow a power law. We show that both the Barabási–Albert and the triadic closure networks exhibit the same behavior. However, for networks based on the triadic closure model, the distributions of FI are more heavy-tailed and, in this sense, are closer to the distributions for real networks.

scholarly journals Detection of the Hardcoded Login Information from Socket and String Compare Symbols

2021 ◽  
Vol 5 (1) ◽  
pp. 28-39
Author(s):  
Minami Yoda ◽  
Shuji Sakuraba ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga
Keyword(s):  
Internet Of Things ◽  
Static Analysis ◽  
Real World ◽  
Symbolic Execution ◽  
The Internet ◽  
User Input ◽  
Network Function ◽  
Private Data ◽  
String Search ◽  
Iot Devices

Internet of Things (IoT) for smart homes enhances convenience; however, it also introduces the risk of the leakage of private data. TOP10 IoT of OWASP 2018 shows that the first vulnerability is ”Weak, easy to predict, or embedded passwords.” This problem poses a risk because a user can not fix, change, or detect a password if it is embedded in firmware because only the developer of the firmware can control an update. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using a static analysis called Socket Search and String Search to protect from first vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, those studies required a lot of time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information: string search and socket search. In string search, the algorithm finds a function that uses the strcmp or strncmp symbol. In socket search, the algorithm finds a function that is referenced by the socket symbol. In this experiment, we measured the ability of our proposed method by searching six firmware in the real world that has a backdoor. We ran three methods: string search, socket search, and whole search to compare the two methods. As a result, all methods found login information from five of six firmware and one unexpected password. Our method reduces the analysis time. The whole search generally takes 38 mins to complete, but our methods finish the search in 4-6 min.

scholarly journals Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

2020 ◽  
Vol 117 (26) ◽  
pp. 14812-14818 ◽  
Author(s):  
Bin Zhou ◽  
Xiangyi Meng ◽  
H. Eugene Stanley
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Statistical Tests ◽  
Finite Size ◽  
Distance Distribution ◽  
Scale Free ◽  
Degree Distance

Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset,Nat. Commun.10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree–degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree–degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree–degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree–degree distance distribution better represents the scale-free property of a complex network.

scholarly journals Identifying the Influential Latent Edges for Promoting the Co-SIR Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dan Yang ◽  
Liming Pan ◽  
Zhidan Zhao ◽  
Tao Zhou
Keyword(s):  
Real World ◽  
Message Passing ◽  
Sir Model ◽  
Research Area ◽  
Disease Outbreak ◽  
Limited Attention ◽  
Original Network ◽  
Spreading Model ◽  
Spreading Dynamics ◽  
Information Spreading

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying the influential latent edges (that is, the edges that do not originally exist) which, if added to the original network, can promote the cooperative susceptible-infected-recovered (co-SIR) dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations.

Relative Assortativity Index: A Quantitative Metric to Assess the Impact of Link Prediction Techniques on Assortativity of Complex Networks

2019 ◽  
Vol 63 (9) ◽  
pp. 1417-1437
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Prediction Technique ◽  
Prediction Techniques ◽  
The Impact

Abstract We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

scholarly journals Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Benjamin Dann ◽  
Jonathan A Michaels ◽  
Stefan Schaffelhofer ◽  
Hansjörg Scherberger
Keyword(s):  
Single Neuron ◽  
Low Frequency ◽  
Small World ◽  
Functional Communication ◽  
Neuron Network ◽  
Active Behavior ◽  
Network Function ◽  
Rich Club ◽  
Spiking Networks ◽  
Unit Network

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

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