TY - GEN
T1 - Towards Quantifying Sampling Bias in Network Inference
AU - Espín-Noboa, Lisette
AU - Wagner, Claudia
AU - Karimi, Fariba
AU - Lerman, Kristina
N1 - Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
PY - 2018/4/23
Y1 - 2018/4/23
N2 - Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labeled sample How should the network be sampled, and what effect does it have on inference error How does the structure of the network impact the sampling strategy We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labeled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. This finding suggests that the impact of network structure on relational classification is more complex than previously thought.
AB - Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labeled sample How should the network be sampled, and what effect does it have on inference error How does the structure of the network impact the sampling strategy We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labeled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. This finding suggests that the impact of network structure on relational classification is more complex than previously thought.
KW - bias
KW - relational classification
KW - sampling networks
UR - http://www.scopus.com/inward/record.url?scp=85085204025&partnerID=8YFLogxK
U2 - 10.1145/3184558.3191567
DO - 10.1145/3184558.3191567
M3 - Conference contribution
AN - SCOPUS:85085204025
T3 - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
SP - 1277
EP - 1285
BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery, Inc
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -