Page 40 - Undergraduate Research Opportunities Program Proceedings 2017-18

School of Science
Department of Physics

Supervisor: SZETO Kwok Yip / PHYS
Student: FENG Zhijie / PHYS-IRE Course: UROP1100, Fall
UROP2100, Spring
UROP3100, Summer

We investigate the stochastic resonance phenomenon and the importance of time scale in opinion formation dynamics
by building an agent-based binary voting model consisting of one-spin-flip damage spreading, random walk searching,
and a well-defined deadline for vote counting. The social network of each party is a Watts-Strogatz (WS) network with
interaction between voters modeled by nearest-interacting Ising model. We model the structure of a strictly fair voting
population by connecting two identical WS networks of opposite spin configurations. The earlier a party introduce the
local disturbance in the enemy’s camp, the higher probability of its winning due to advantage in time. On the other hand,
the party (the latecomer) that introduces the local disturbance later has the advantage in searching for the best place to
inflict the damage, such as the information of node degree gathered from preliminary random walk searching. The
competition between advantage in time and advantage in information reveals the existence of an optimum searching
time for the latecomer to win. The winning probability of the party are measured at the deadline after we allow the
introduction of two counterbalancing local disturbances, by sudden flipping of opinion configuration, at two different
times during the interaction. In this work, we show the significance of node’s degree, and study how the parameters,
including (1) deadline, (2) network randomness, (3) winning threshold, and (4) temperature, affect the dependence of
winning on time by numerical simulation. For the effect of temperature, we notice an optimum temperature that induce
longest range of winning time for the latecomer, and propose a comparative study with prototype stochastic resonance
for the phenomenon in our model.

Supervisor: SZETO Kwok Yip / PHYS
Student: GUO Wenshuo / PHYS Course: UROP1100, Spring

This report serves as a progress report for UROP1100: Random Walk on Complex Network and Application to Numerical
Simulation for statistical physics. In this semester, Guo did various explorations on random walk topic. This report
contains mainly three parts: In the first part, an interesting model on Random Walk for Opinion Formation in Complex
Networks is presented. This part of work is an outcome collaborated with Sarah during the semester. Guo purposed the
model, Sarah performed the numerical simulations and Guo helped check and fix the program. In the second part, we
provide a summary of the study progress on complex network and random walk. In the last section, Guo included her
learning on statistical inference during this semester.

Supervisor: SZETO Kwok Yip / PHYS
Student: YIP Chun Yin / PHYS Course: UROP4100, Spring
In the present report, the topological charge of a node in a network is defined as the difference between its degree and
the average degree. In particular, the report focused on the WS network model, which is often used to describe a social
network. The report then devised various types of topological-charge-biased random walkers, who preferred to go to a
neighbouring node with either a like or an opposite charge. The walkers’ cover time and cover speed on WS networks
were studied. It was observed that the performance of likecharge-biased walkers were slightly different from that of
opposite-charge-biased walkers. The reason for this phenomenon is yet to be answered.

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