TY - JOUR
T1 - Signed network representation with novel node proximity evaluation
AU - Xu, Pinghua
AU - Hu, Wenbin
AU - Wu, Jia
AU - Liu, Weiwei
PY - 2022/4
Y1 - 2022/4
N2 - Currently, signed network representation has been applied to many fields, e.g., recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks — sign prediction and link prediction. The code is publicly available.
AB - Currently, signed network representation has been applied to many fields, e.g., recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks — sign prediction and link prediction. The code is publicly available.
KW - Signed social network
KW - Network representation
KW - Node proximity
UR - http://www.scopus.com/inward/record.url?scp=85124032027&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DE200100964
U2 - 10.1016/j.neunet.2022.01.014
DO - 10.1016/j.neunet.2022.01.014
M3 - Article
C2 - 35131567
AN - SCOPUS:85124032027
SN - 0893-6080
VL - 148
SP - 142
EP - 154
JO - Neural Networks
JF - Neural Networks
ER -