Abstract
Signed social networks have both positive and negative links which convey rich information such as trust or distrust, like or dislike. However, existing network embedding methods mostly focus on unsigned networks and ignore the negative interactions between users. In this paper, we investigate the problem of learning representations for signed networks and present a novel deep network structure to incorporate both the balance and status theory in signed networks. With the proposed framework, we can simultaneously learn the node embedding encoding the status of a node and the edge embedding denoting the sign of an edge. Furthermore, the learnt node and edge embeddings can be directly applied to the sign prediction and node ranking tasks. Experiments on real-world social networks demonstrate that our model significantly outperforms the state-of-the-art baselines.
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Change history
01 July 2018
The original version of this chapter titled “Free-Rider Episode Screening via Dual Partition Model” contained the following three mistakes:
- 1.
In table 1, row 2, column 3, the average occurrence per event on STK dataset was “1.037”. It should be “1,037”.
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The last model name in the legend of Figure 3 was “EIP”. It should be “EDP”.
- 3.
In the experiment part the stock symbols and their companies were confused.
In the updated version these mistakes were corrected.
In the originally published version of chapters titled “BASSI: Balance and Status Combined Signed Network Embedding” and “Sample Location Selection for Efficient Distance-Aware Influence Maximization in Geo-Social Networks” the funding information in the acknowledgement section was incomplete. This has now been corrected.
- 1.
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Acknowledgement
The work described in this paper has been supported in part by the NSFC projects (61572376, 91646206), the 111 project (B07037), and Natural Science Foundation of Hubei Province under Grant No. 2018CFB616.
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Chen, Y., Qian, T., Zhong, M., Li, X. (2018). BASSI: Balance and Status Combined Signed Network Embedding. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_4
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