  Tie strength prediction, sometimes named weight prediction, is vital in
exploring the diversity of connectivity pattern emerged in networks. Due to the
fundamental significance, it has drawn much attention in the field of network
analysis and mining. Some related works appeared in recent years have
significantly advanced our understanding of how to predict the strong and weak
ties in the social networks. However, most of the proposed approaches are
scenario-aware methods heavily depending on some special contexts and even
exclusively used in social networks. As a result, they are less applicable to
various kinds of networks.
  In contrast to the prior studies, here we propose a new computational
framework called Neighborhood Estimating Weight (NEW) which is purely driven by
the basic structure information of the network and has the flexibility for
adapting to diverse types of networks. In NEW, we design a novel index, i.e.,
connection inclination, to generate the representative features of the network,
which is capable of capturing the actual distribution of the tie strength. In
order to obtain the optimized prediction results, we also propose a
parameterized regression model which approximately has a linear time complexity
and thus is readily extended to the implementation in large-scale networks. The
experimental results on six real-world networks demonstrate that our proposed
predictive model outperforms the state of the art methods, which is powerful
for predicting the missing tie strengths when only a part of the network's tie
strength information is available.
