activity
20112019
most citedWeighted reciprocity in human communication networks

12 citations · 34 across the 7 of their papers we have counts for

collaborators
Showing cs.SIShow all

5 papers · 1 filter

cs.SI2019★ 3 cited

The power of dynamic social networks to predict individuals' mental health

Shikang Liu, David Hachen, Omar Lizardo +3

Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their…

cs.SI2019★ 4 cited

Heterogeneous network approach to predict individuals' mental health

Shikang Liu, Fatemeh Vahedian, David Hachen +4

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predic…

cs.SI2014★ 6 cited

Lessons Learned from an Experiment in Crowdsourcing Complex Citizen Engineering Tasks with Amazon Mechanical Turk

Matthew Staffelbach, Peter Sempolinski, David Hachen +5

We investigate the feasibility of obtaining highly trustworthy results using crowdsourcing on complex engineering tasks. Crowdsourcing is increasingly seen as a potentially powerfu…

cs.SI2011★ 12 cited

Weighted reciprocity in human communication networks

Cheng Wang, Anthony Strathman, Omar Lizardo +3

In this paper we define a metric for reciprocity---the degree of balance in a social relationship---appropriate for weighted social networks in order to investigate the distributio…

cs.SI2011

Predictors of short-term decay of cell phone contacts in a large scale communication network

Troy Raeder, Omar Lizardo, David Hachen +1

Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + Delta(t)? Previous research addressing this issue suggests…