Hai Zhao

Characterizing and modeling the Internet Router-level topology - The hierarchical features and HIR model

In this paper, we seek to understand the properties related with the hierarchical structure of the Internet Router-level topology. We first analyzed the hierarchical features of the Internet Router-level topology with the actual topology measuring data authorized by CAIDA (The Cooperative Association for Internet Data Analysis) Skitter project. Then we analyzed the distribution of the nodes in each shell, the distribution of the edges within the shell and to the higher shells and induced the computing functions. We proposed a new framework for modeling the Internet Router-level topology with the conclusion we have drawn and we implemented the model, HIR model. HIR model was based on the k-core decomposition in order to reveal the hierarchical of the network. The analysis shows that HIR model can reproduce Internet on most properties and has got the hierarchies of network close to that of Internet. The experiments show that HIR model can fully reflect the hierarchy characteristics of the Internet Router-level topology while preserving the power-law distribution on degree.

Symmetric Preferential Attachment for New Vertices Attaching to Software Networks

The evolution of open-source software systems can be regarded as the process of self-organization. Most existing models for software network evolution are based on preferential attachment. However, our empirical studies on the attachment of new vertices show that preferential attachment is not completely suitable for the formation of software networks. In this paper, the attachment mechanism of new vertices is studied in a set of real-world software systems. Firstly, these software systems are treated as directed networks, then based on our empirical studies on the connecting direction between the new vertices and existing networks, a mechanism of symmetric preferential attachment in software network evolution is proposed. It is worth mentioning that the probabilities for a vertex to acquire new incoming and outgoing connections are proportional to its in-degree and out-degree, respectively. Furthermore, the probability density functions of in-degree and out-degree distributions deduced through theoretical computations show that symmetric preferential attachment can produce scale-free networks, and the value ranges of the given exponent expressions are proved to be in accordance with those of various real-world software networks. Finally, the relationship between the exponents of in-degree and out-degree distributions and existing code reuse in software design and development is revealed. This work could provide a different perspective to observe potential formation and integration of real-world software systems.

Extraction and Analysis of Crucial Fraction in Software Networks

Many complex systems, such as software systems, are full of complexity arising from interactions among basic units (such as classes, interfaces and struts in object-oriented software systems). One of the most successful approaches to capture the underlying structural features of large-scale software systems is the investigation of hierarchical organization. However, the hierarchy of software networks has not been thoroughly investigated. In this paper, the crucial fraction (CF) in software networks has been extracted and analyzed in a set of real-world software systems. First, the classes and the relationships between them have been extracted into software networks. Then software networks have been divided into different layers, and CF of software networks has been extracted by k-core. The empirical studies in this paper reveal that software networks represent flat hierarchical structure. Finally, CF has been measured by the relevant complex network parameters respectively, and the relations between CF and overall network have been analyzed by the case studies of software networks. The results show that CF represents characteristics of scale-free, small-world, strong connectivity, and the units in CF are frequently reused and dominate the overall system.

The K-Core Decomposition and Visualization of Internet Router-Level Topology

The research was based on the massive data authorized by CAIDA (The Cooperative Association for Internet Data Analysis) Skitter project. We used the k-core decomposition to disentangle the hierarchical structure of Internet router-level topology. Combining with the actual topology measuring data, we first exhibited the pruning procedure of Internet router-level topology. Then we analyzed the characteristics in every shell which was pruned. It is concluded that the number of the nodes, isolated nodes and clusters in every shell decrease with power law. At last, we exhibited the evolvement of the Internet topology by the evolving of the largest sub-net from the innermost core to the outermost core. During the evolvement, we can easily observe the clustering properties of Internet topology.

An Adaptive Classified Data Aggregation Arithmetic for Wireless Sensor Networks

Energy limitation is a crucial problem in wireless sensor networks. The researches show that the energy is mainly consumed during data transmission. However, data aggregation can make an optimization of the apperceiving data to constrain the redundancy data for avoiding unnecessary energy consumption. Generally, a sensor node is combined with diversified sensors, hence, it is unreasonable to collect and transmit the sensing data in same frequency. This paper proposed a kind of adaptive classified data aggregation arithmetic which can classify the different sensing data, control the transmission according to the dynamically threshold set, and complete the data aggregation by adjusting data quantity along the transmission path. Different sets of parameters were conducted in these experiments to test the property of sensor network adopting the proposed arithmetic. The experiment results have verified the efficient energy utilization.

Virus spreading model based on degree correlation and its analysis

In recent years the virus spreading has produced a series threat to network security. The research shows that the Internet topology has a great relationship with the virus spreading. The degree correlation is as an important characteristic of the Internet topology, its changes means changing the Internet topology structure. According to analyzing the degree characteristic correlated, we find that the Internet disassortativity is weaken. The experiments about virus spreading are done on the networks what are produced by DPR algorithm under different associativity coefficient aiming to study the spreading rate, steady infection rate and spreading critical value; Then a virus spreading model suited for the Internet is proposed based on the traditional virus spreading model SIS (susceptible-infected-susceptible). We do the experiments about the virus spreading and analyze the characteristic of virus spreading under steady and transient. Finally the protection policy of the virus spreading is discussed according to the simulation experiments results.

The Analysis and Modeling on Internet AS-Level Topology Based on K-Core Decomposition

The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. The properties related with node coreness were studied firstly. The distribution on coreness of Internet AS-level topology was analyzed with the actual topology measuring data. The linking trend to higher shells of the nodes was analyzed and the linking probability function was induced. Then, a new framework for modeling Internet AS-level topology was proposed with the conclusion drawn and the model was implemented. The proposed model was based on k-core decomposition in order to reveal the hierarchy of the network. Experiment results show that the proposed model produces scale-free random graphs, in the meanwhile, it also exhibits the hierarchy of the network better.

Research on LFS Algorithm in Software Network

Betweenness centrality helps researcher to master the changes of the system from the overall perspective in software network. The existing betweenness centrality algorithm has high time complexity but low accuracy. Therefore, Layer First Searching (LFS) algorithm is proposed that is low in time complexity and high in accuracy. LFS algorithm searches the nodes with the shortest to the designated node, then travels all paths and calculates the nodes on the paths, at last get the times of each node being traveled which is betweenness centrality. The time complexity of LFS algorithm is O(V2).

One immunization strategy of complex networks

Aiming at improving respective shortcoming of target immunization and acquaintance immunization strategy, we present an immunization strategy which consists of two steps: choosing some node randomly and then adopting strengthened acquaintance immunization or improved target immunization according to different circumstances. This synthesis strategy retains the advantage of making decision based on pure local information, without needing information of global network structure or identification of the highest degree nodes. When the percentage of required vaccinations for immunity is the same as the targeted immunization, we get better results. Compare with other efficient strategies on a scale-free network model, the proposed strategy is significantly more effective and adaptive.

On the internet AS-level topology evolution basing on hierarchy characteristic

The analysis on Internet topological structure is crucial for real application and further development of network. By the analysis of a long period of AS-level topology data, the evolution relationship between the major network characteristics and corresponding node degrees are deduced. Meanwhile, several important network hierarchical characteristics and the influence of high-level ASes on the network topology are obtained by the analysis on snapshot data. Using hierarchical model, this paper proves that the networkpsilas hierarchy is important to its topology. With the analysis results of two aspects, we draw the conclusion that Internet AS-level topology shows a very slow homogenization and the high-level nodes represented by high-level ASes are the main reason for network topologypsilas stability, such as cluster, power-law and hierarchy.