Graph representation learning serves as the core of important prediction tasks, ranging from
product recommendation to fraud detection. Real-life graphs usually have complex …

Seeding the most influential individuals based on the contact structure can substantially
enhance the extent of a spread over the social network. Most of the influence maximization …

Many technological, socio-economic, environmental, biomedical phenomena exhibit an
underlying graph structure. Valued graph allows one to incorporate the connections or links …

Representing a collection of relevant units by a graph allows one to incorporate the
connections (or links) among the units in addition to the units themselves. One may be …

Slums have long been a feature of urban life in Pakistan as well as throughout the globe.
Nearly all slum locations may be found outside of cities. Slums, however, are prevalent in …

We propose a family of lagged random walk sampling methods in simple undirected graphs,
where transition to the next state (ie, node) depends on both the current and previous states …

In bipartite incidence graph sampling, the target study units may be formed as connected
population elements, which are distinct to the units of sampling and there may exist …

Network data often contain sensitive relational information. One approach to protecting
sensitive information while offering flexibility for network analysis is to share synthesized …

Bipartite incidence graph sampling provides a unified representation of many sampling
situations for the purpose of estimation, including the existing unconventional sampling …

We develop lagged Metropolis–Hastings walk for sampling from simple undirected graphs
according to given stationary sampling probabilities. It is explained how the technique can …