Graph sampling is a technique to pick a subset of vertices and/or edges from original graph.
It has a wide spectrum of applications, eg survey hidden population in sociology [54] …

With more than 250 million active users, Facebook (FB) is currently one of the most
important online social networks. Our goal in this paper is to obtain a representative …

Network sampling is integral to the analysis of social, information, and biological networks.
Since many real-world networks are massive in size, continuously evolving, and/or …

Estimating characteristics of large graphs via sampling is a vital part of the study of complex
networks. Current sampling methods such as (independent) random vertex and random …

Security concerns are becoming increasingly critical in networked systems. Firewalls
provide important defense for network security. However, misconfigurations in firewalls are …

Our goal in this paper is to develop a practical framework for obtaining a uniform sample of
users in an online social network (OSN) by crawling its social graph. Such a sample allows …

Social media embeddedness relationships consist of online social networks formed by self-
organized individual actors and significantly affect many aspects of our lives. Since the high …

Breadth First Search (BFS) and other graph traversal techniques are widely used for
measuring large unknown graphs, such as online social networks. It has been empirically …

Breadth First Search (BFS) is a widely used approach for sampling large graphs. However, it
has been empirically observed that BFS sampling is biased toward high-degree nodes …

Graph sampling via crawling has been actively considered as a generic and important tool
for collecting uniform node samples so as to consistently estimate and uncover various …