We introduce a framework for the modeling of sequential data capturing pathways of varying
lengths observed in a network. Such data are important, eg, when studying click streams in …

Accurate human mobility prediction is important for many applications in wireless networks,
including intelligent content caching and prefetching, network optimization, etc. However …

A second-order random walk on a graph or network is a random walk where transition
probabilities depend not only on the present node but also on the previous one. A notable …

Users form information trails as they checkin with a geolocation, rate items, or consume
media. A common problem is to predict what a user might do next for the purposes of …

Detection of changes in streaming data is an important mining task, with a wide range of real-
life applications. Numerous algorithms have been proposed to efficiently detect changes in …

This work derives a theoretical value for the entropy of a Linear Additive Markov Process
(LAMP), an expressive but simple model able to generate sequences with a given …

We propose a new framework for sequential decision making, Polya Decision Processes
(PDP); it can express the agent's history-dependent transitions by using the Polya urn …

In this paper, we propose a new probabilistic model called mixture of linear additive Markov
processes (MoL) that can analyze sequential data such as histories of people's visit …

Understanding human navigation behavior has implications for a wide range of application
scenarios. For example, insights into geo-spatial navigation in urban areas can impact city …

The availability of modern technology and the recent proliferation of devices and sensors
have resulted in a tremendous amount of data being generated, stored and handled in …