TY - GEN
T1 - Link prediction across networks by biased cross-network sampling
AU - Qi, Guo Jun
AU - Aggarwal, Charu C.
AU - Huang, Thomas
PY - 2013
Y1 - 2013
N2 - The problem of link inference has been widely studied in a variety of social networking scenarios. In this problem, we wish to predict future links in a growing network with the use of the existing network structure. However, most of the existing methods work well only if a significant number of links are already available in the network for the inference process. In many scenarios, the existing network may be too sparse, and may have too few links to enable meaningful learning mechanisms. This paucity of linkage information can be challenging for the link inference problem. However, in many cases, other (more densely linked) networks may be available which show similar linkage structure in terms of underlying attribute information in the nodes. The linkage information in the existing networks can be used in conjunction with the node attribute information in both networks in order to make meaningful link recommendations. Thus, this paper introduces the use of transfer learning methods for performing cross-network link inference. We present experimental results illustrating the effectiveness of the approach.
AB - The problem of link inference has been widely studied in a variety of social networking scenarios. In this problem, we wish to predict future links in a growing network with the use of the existing network structure. However, most of the existing methods work well only if a significant number of links are already available in the network for the inference process. In many scenarios, the existing network may be too sparse, and may have too few links to enable meaningful learning mechanisms. This paucity of linkage information can be challenging for the link inference problem. However, in many cases, other (more densely linked) networks may be available which show similar linkage structure in terms of underlying attribute information in the nodes. The linkage information in the existing networks can be used in conjunction with the node attribute information in both networks in order to make meaningful link recommendations. Thus, this paper introduces the use of transfer learning methods for performing cross-network link inference. We present experimental results illustrating the effectiveness of the approach.
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U2 - 10.1109/ICDE.2013.6544875
DO - 10.1109/ICDE.2013.6544875
M3 - Conference contribution
AN - SCOPUS:84881335106
SN - 9781467349086
T3 - Proceedings - International Conference on Data Engineering
SP - 793
EP - 804
BT - ICDE 2013 - 29th International Conference on Data Engineering
T2 - 29th International Conference on Data Engineering, ICDE 2013
Y2 - 8 April 2013 through 11 April 2013
ER -