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Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. In this context, similaritybased algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, which is based on the node centrality of common neighbors. Three node centralities are discussed: degree, closeness and betweenness centrality. In our model, each common neighbor plays a different role to the node connection likelihood according to their centralities. Moreover, the weak-tie theory is considered for improving the prediction accuracy. Finally, extensive experiments on five real-world networks show that the proposed model can outperform the Common Neighbor (CN) algorithm and gives competitively good prediction of or even better than Adamic-Adar (AA) index and Resource Allocation (RA) index.

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In order to make clear the recent status and trend of wheat (Triticum aestivum L.) production in China, datasets from multiple field experiments and published literature were collected to study the agronomic characteristics related to grain yield, fertilizer application and nutrient use efficiency from the year 2000 to 2011. The results showed that the mean grain yield of wheat in 2000–2011 was 5950 kg/ha, while the N, P2O5 and K2O application rates were 172, 102 and 91 kg/ha on average, respectively. The decrease in N and P2O5 and increase in K2O balanced the nutrient supply and was the main reason for yield increase. The partial factor productivity (PFP, kg grain yield produced per unit of N, P2O5 or K2O applied) values of N (PFP-N), P (PFP-P) and K (PFP-K) were in the ranges of 29.5~39.6, 43.4~74.9 and 44.1~76.5 kg/kg, respectively. While PFP-N showed no significant changes from 2000 to 2010, both PFP-P and PFP-K showed an increased trend over this period. The mean agronomic efficiency (AE, kg grain yield increased per unit of N, P2O5 or K2O applied) values of N (AEN), P (AEP) and K (AEK) were 9.4, 10.2 and 6.5 kg/kg, respectively. The AE values demonstrated marked inter-annual fluctuations, with the amplitude of fluctuation for AEN greater than those for AEP and AEK. The mean fertilizer recovery efficiency (RE, the fraction of nutrient uptake in aboveground plant dry matter to the nutrient of fertilizer application) values of N, P and K in the aboveground biomass were 33.1%, 24.3% and 28.4%, respectively. It was also revealed that different wheat ecological regions differ greatly in wheat productivity, fertilizer application and nutrient use efficiency. In summary, it was suggested that best nutrient management practices, i.e. fertilizer recommendation applied based on soil testing or yield response, with strategies to match the nutrient input with realistic yield and demand, or provided with the 4R’s nutrient management (right time, right rate, right site and right fertilizer) should be adopted widely to improve the yield production and nutrient use efficiency.

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Knowledge graph representation has been a long standing goal of artificial intelligence. In this paper, we consider a method for knowledge graph embedding of hyper-relational data, which are commonly found in knowledge graphs. Previous models such as Trans (E, H, R) and CTransR are either insufficient for embedding hyper-relational data or focus on projecting an entity into multiple embeddings, which might not be effective for generalization nor accurately reflect real knowledge. To overcome these issues, we propose the novel model TransHR, which transforms the hyper-relations in a pair of entities into an individual vector, serving as a translation between them. We experimentally evaluate our model on two typical tasks-link prediction and triple classification.The results demonstrate that TransHR significantly outperforms Trans (E, H, R) and CTransR, especially for hyperrelational data.

-With the help of information and communication technologies, studies on the overall social networks have been extensively reported recently. However, investigations on the directed Ego Communication Networks (ECNs) remain insufficient, where an ECN stands for a sub network composed of a centralized individual and his/her direct contacts. In this paper, the directed ECNs are built on the Call Detail Records (CDRs), which cover more than 7 million people of a provincial capital city in China for half a year. Results show that there is a critical size for ECN at about 150, above which the average emotional closeness between ego and alters drops, the balanced relationship between ego and network collapses, and the proportion of strong ties decreases. This paper not only demonstrate the significance of ECN size in affecting its properties, but also shows accordance with the "Dunbar's Number". These results can be viewed as a cross-culture supportive evidence to the well-known Social Brain Hypothesis (SBH).