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Homa Hosseinmardi
University of Southern California
Internet privacyMachine learningData miningComputer scienceSocial network
32Publications
10H-index
400Citations
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Publications 31
Newest
#1Shen Yan (SC: University of Southern California)H-Index: 2
#2Homa Hosseinmardi (SC: University of Southern California)H-Index: 10
Last. Emilio Ferrara (SC: University of Southern California)H-Index: 38
view all 6 authors...
Affective states are associated with people’s mental health status and have profound impact on daily life, thus unobtrusively understanding and estimating affects have been brought to the public attention. The pervasiveness of wearable sensors makes it possible to build automatic systems for affect tracking. However, constructing such systems is a challenging task due to the complexity of human behaviors. In this work, we focus on the problem of estimating daily self-reported affects from sensor...
Source
#1Nazgol TavabiH-Index: 3
#2Homa HosseinmardiH-Index: 10
Last. Kristina LermanH-Index: 45
view all 7 authors...
The ubiquity of mobile devices and wearable sensors offers unprecedented opportunities for continuous collection of multimodal physiological data. Such data enables temporal characterization of an individual's behaviors, which can provide unique insights into her physical and psychological health. Understanding the relation between different behaviors/activities and personality traits such as stress or work performance can help build strategies to improve the work environment. Especially in work...
#1Amir GhasemianH-Index: 3
#2Homa HosseinmardiH-Index: 10
Last. Aaron ClausetH-Index: 33
view all 5 authors...
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current method...
1 Citations
#1Shen Yan (SC: University of Southern California)H-Index: 2
#2Homa Hosseinmardi (SC: University of Southern California)H-Index: 10
Last. Emilio Ferrara (SC: University of Southern California)H-Index: 38
view all 6 authors...
Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an explosion in popularity. Their pervasiveness allows for effective data collections to quantify human behavior in natural settings, enriching traditional behavioral science research opportunities. In this paper, we focus on the problem of affect estimation from sensor-generated data, whereas ground truth is available to us in the form of daily self-reported affective states. First, our analysis shows that individu...
2 CitationsSource
#1Homa Hosseinmardi (ISI: Information Sciences Institute)H-Index: 10
#2Hsien-Te Kao (SC: University of Southern California)H-Index: 2
Last. Emilio Ferrara (SC: University of Southern California)H-Index: 38
view all 4 authors...
In recent years, the rapid growth in technology has increased the opportunity for longitudinal human behavioral studies. Rich multimodal data, from wearables like Fitbit, online social networks, mobile phones etc. can be collected in natural environments. Uncovering the underlying low-dimensional structure of noisy multi-way data in an unsupervised setting is a challenging problem. Tensor factorization has been successful in extracting the interconnected low-dimensional descriptions of multi-way...
5 Citations
May 13, 2019 in WWW (The Web Conference)
#1Hsien-Te Kao (SC: University of Southern California)H-Index: 2
#2Shen Yan (SC: University of Southern California)H-Index: 2
Last. Emilio Ferrara (SC: University of Southern California)H-Index: 38
view all 6 authors...
Cyberbullying is a major issue on online social platforms, and can have prolonged negative psychological impact on both the bullies and their targets. Users can be characterized by their involvement in cyberbullying according to different social roles including victim, bully, and victim supporter. In this work, we propose a social role detection framework to understand cyberbullying on online social platforms, and select a dataset that contains users’ records on both Instagram and Ask.fm as a ca...
1 CitationsSource
#1Amir Ghasemian (CU: University of Colorado Boulder)H-Index: 3
#2Homa Hosseinmardi (SC: University of Southern California)H-Index: 10
Last. Aaron Clauset (CU: University of Colorado Boulder)H-Index: 33
view all 3 authors...
A common graph mining task is community detection, which seeks an unsupervised decomposition of a network into groups based on statistical regularities in network connectivity. Although many such algorithms exist, community detection's No Free Lunch theorem implies that no algorithm can be optimal across all inputs. However, little is known in practice about how different algorithms over or underfit to real networks, or how to reliably assess such behavior across algorithms. Here, we present a b...
28 CitationsSource
#1Palash Goyal (SC: University of Southern California)H-Index: 8
#2Homa Hosseinmardi (SC: University of Southern California)H-Index: 10
Last. Aram Galstyan (SC: University of Southern California)H-Index: 30
view all 4 authors...
Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, in practice, we often have auxiliary information about the nodes and/or their interactions, e.g., the content of scientific papers in coauthorship networks, or topics of communication in Twitter mention networks. Here, we propose a...
6 CitationsSource
#1Hsien-Te Kao (SC: University of Southern California)H-Index: 2
#2Homa Hosseinmardi (SC: University of Southern California)H-Index: 10
Last. Emilio Ferrara (SC: University of Southern California)H-Index: 38
view all 7 authors...
Hospitals are high-stress environments where workers face a high risk of occupational burnout due to a mix of imbalanced schedules, understaffing, and emotional stress. In this paper, we propose a computational framework to infer the latent psychological makeup and traits of hospital workers. We apply machine learning models to psychometric data obtained from a suite of psychological survey instruments, collected as a part of TILES, a ten-week research study carried out in a large Los Angeles ho...
2 CitationsSource
#1Homa HosseinmardiH-Index: 10
#2Amir GhasemianH-Index: 3
Last. Emilio FerraraH-Index: 38
view all 5 authors...
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual, and insightful assessments of individual performance and wellbeing? Prediction of different aspects of human behavior from these noisy, incomplete, and heterogeneous bio-behavioral te...
1 Citations
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