Application of Link Prediction in Temporal Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2231-2236 ◽  
Author(s):  
Hai Hang Xu ◽  
Li Jun Zhang

Link prediction is an important research hotspot in complex networks.Correlational studies merely use static topology for prediction, without considering the influence of network dynamic evolutionary process on link prediction. We believe that the linksare derived from the evolutionary process of network, and dynamic network topology will contain more information, Moreover, many networks have time attribute naturally, which is apt to combine the similarity of time and structure for link prediction. The paper proposes the concept of active factor using time attribute, to extend the similaritybased link prediction framework.Thenmodeland analysis the data of citation network and cooperation network with temporal networks.Design the active factors for both network sand verify the performance of these new indexes. The results shows that the indexes with active factor perform better than structure similarity based indexes.

2017 ◽  
Vol 28 (06) ◽  
pp. 1750082 ◽  
Author(s):  
Yang Ma ◽  
Guangquan Cheng ◽  
Zhong Liu ◽  
Xingxing Liang

Link prediction in social networks has become a growing concern among researchers. In this paper, the clustering method was used to exploit the grouping tendency of nodes, and a clustering index (CI) was proposed to predict potential links with characteristics of scientific cooperation network taken into consideration. Results showed that CI performed better than the traditional indices for scientific coauthorship networks by compensating for their disadvantages. Compared with traditional algorithms, this method for a specific type of network can better reflect the features of the network and achieve more accurate predictions.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29219-29230 ◽  
Author(s):  
Taisong Li ◽  
Jiawei Zhang ◽  
Philip S. Yu ◽  
Yan Zhang ◽  
Yonghong Yan

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Haifeng Jiang ◽  
Xiaoxiao Liu ◽  
Shuo Xiao ◽  
Chaogang Tang ◽  
Wei Chen

Mobile Ad Hoc Network (MANET) is suitable for complex environment communication in coal mine. The processes of nutrient flux transfer and path choice in Physarum networks are similar to data transmission and routing decision in MANET. In this paper, we use a Physarum optimization model to design Physarum-inspired autonomous optimized routing (PIAOR) protocol to adapt to the dynamic network topology in underground mine. PIAOR introduces the status of MANET into the Poisson equation in the Physarum model, selects reasonable parameters to represent the transmission performance of the network, and uses the differential evolution equation of the Physarum model to evolve the parameters. PIAOR has achieved the distributed routing decision by automatically reconstructing the optimal routing path, which has reduced the algorithm complexity. Based on NS2, simulation experiments are performed to evaluate the performance of PIAOR, and the results are compared with GPSR, PIMAR, and P-IRP routing algorithms. The experimental results show that the routing path selected by PIAOR is better than that selected by the other three protocols in the performance of average end-to-end delay, delivery ratio, and throughput. The balance of energy consumption and network load is reached, and the network lifetime is effectively prolonged when using the PIAOR protocol.


1975 ◽  
Vol 40 (312) ◽  
pp. 405-414 ◽  
Author(s):  
D. K. Bailey ◽  
R. Macdonald

SummaryFluorine, chlorine, zinc, niobium, zirconium, yttrium, and rubidium have been deter-mined on fifteen obsidians from Eburru volcano (Kenya Rift Valley), spanning the range from pantel-leritic trachyte to pantellerite. All pairs of elements show positive correlation coefficients, ranging between 0·769 and 0·998, but with most values better than 0·900. In spite of some very high correlations, only two of the twenty-one best-fit lines pass near the origin of the Cartesian coordinates. Linear distributions are found within two separate groups of elements: F, Zr, Rb; and Cl, Nb, Yt. Zn behaves in general as a member of the second group but seems to be subject to an additional variation. When an element from the fluorine group is plotted against one from the chlorine group the resulting pattern is non-linear. Therefore, although the elements in both groups would generally be considered ‘residual’ (partition coefficients between crystals and liquid approaching zero) there are clearly detectable differences in their variation, and hence their behaviour.Major-element variations in the obsidians are such that a vapour (fluid) phase would be needed to account for any magma evolution. The trace-element patterns are also impossible by closed-system crystal fractionation and suggest that this fluid may have been rich in halogens, with the metallic elements forming preferred ‘complexes’ with either F or Cl. The F-Zr-Rb ‘complex’ also varies quite independently of the important major oxides (e.g. A12O3) in the rocks. In the case of Rb this is but one aspect of a more significant anomaly, in which there is no sign of any influence of alkali feldspar (which partitions Rb) in the variation. This is remarkable because trachytes and rhyolites have normative ab+or > 50 %, and any evolutionary process controlled by crystal ⇋ liquid interactions must be dominated by the melting or crystallization of alkali feldspar. The results on the Eburru obsidians show that if they are an evolutionary series then either, the process was not crystal ⇋ liquid controlled, or that any such process has been overriden (or buffered) by other processes that have superimposed the observed trace-element patterns. In the latter event, the buffering phase may have been a halogen-bearing vapour.The same considerations must apply to other pantellerite provinces where Rb appears to have behaved as a ‘residual’ element.


2014 ◽  
Vol 17 (07n08) ◽  
pp. 1550005 ◽  
Author(s):  
QI XUAN ◽  
CHENBO FU ◽  
LI YU

In open source software (OSS) projects, participants initially communicate with others and then may become developers if they are deemed worthy by the community. Recent studies indicate that the abundance of established social links of a participant is the strongest predictor to his/her promotion. Having reliable rankings of the candidates is key to recruiting and maintaining a successful operation of an OSS project. This paper adopts degree-based, PageRank, and Hits ranking algorithms to rank developer candidates in OSS projects based on their social links. We construct several types of social networks based on the communications between the participants in Apache OSS projects, then train and test the ranking algorithms in these networks. We find that, for all the ranking algorithms under study, the rankings of emergent developers in temporal networks are higher than those in cumulative ones, indicating that the more recent communications of a developer in a project are more important to predict his/her first commit in the project. By comparison, the simple degree-based and the PageRank ranking algorithms in temporal undirected weighted networks behave better than the others in identifying emergent developers based on four performance indicators, and are thus recommended to be applied in the future.


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