[1]SU Xiaoping,SONG Yurong.Local labeling features and a prediction method for a signed network[J].CAAI Transactions on Intelligent Systems,2018,13(3):437-444.[doi:10.11992/tis.201710027]
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Local labeling features and a prediction method for a signed network
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 3
Page number:
437-444
Column:
学术论文—智能系统
Public date:
2018-05-05
- Title:
- Local labeling features and a prediction method for a signed network
- Keywords:
- signed networks; sign prediction; low rank; matrix factorization; signed bias; structural balance theory; weak structural balance theory; status theory
- CLC:
- TP399
- DOI:
- 10.11992/tis.201710027
- Abstract:
- A complex network may be considered as a symbol network when links have a positive or negative sign attribute. In signed social networks, positive links represent a trust (friends) relationship between users. In contrast, negative links indicate distrust (hostility). An important task in a signed network is to define a signed network based on partial observation to predict an unknown symbol. Through analysis, we found that for a signed network with weak structural balance, its adjacent matrix has a global low-rank characteristic and the prediction of the link sign can be approximated as a low-rank matrix factorization. However, in a basic low-rank model, it is difficult to sufficiently utilize the local behavior features for labeling the signs of links between the neighboring nodes. Herein, a low-rank matrix factorization model with bias was proposed. In this model, the sign features of the exit and entry links of a neighboring node were introduced to improve the precision of sign prediction. Experiments based on real data revealed that the low-rank model with bias can obtain better prediction results than other benchmark algorithms and that the proposed algorithm performed with a high efficiency.
- References:
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Last Update:
2018-06-25