Scalable adaptive label propagation in Grappa

Nodes of a social graph often represent entities with specific labels, denoting properties such as age-group or gender. Design of algorithms to assign labels to unlabeled nodes by leveraging node-proximity and a-priori labels of seed nodes is of significant interest. A semi-supervised approach to solve this problem is termed ``LPA-Label Propagation Algorithm'' where labels of a subset of nodes are iteratively propagated through the network to infer yet unknown node labels. While LPA for node labelling is extremely fast and simple, it works well only with an assumption of node-homophily -- connected nodes are connected because they must deserve a similar label -- which can often be a misnomer. In this paper we propose a novel algorithm ``Adaptive Label Propagation'' that dynamically adapts to the underlying characteristics of homophily, heterophily, or otherwise, of the connections of the network, and applies suitable label propagation strategies accordingly. Moreover, our adaptive label propagation approach is scalable as demonstrated by its implementation in Grappa, a distributed shared-memory system. Our experiments on social graphs from Facebook, YouTube, Live Journal, Orkut and Netlog demonstrate that our approach not only improves the labelling accuracy but also computes results for millions of users within a few seconds

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement

Background: Low intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation (tACS or tDCS), applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional magnetic resonance imaging (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. Objective: To develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency, and reproducibility (ContES Checklist). Methods: A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists (EP) through the International Network of the tES-fMRI (INTF) Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC based on a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed using the checklist. Results: Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (1) technological factors, (2) safety and noise tests, and (3) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. Conclusions: Use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies, and increase methodological transparency and reproducibility

Who Are You? ...Honestly!

Online social media has made life easier in many ways. Stay in touch with old classmates (Facebook), find a way to connect to like-minded professionals all over the globe (LinkedIn), and even seek help from fellow humans we could never imagine otherwise to help us (Mechanical Turk)! Social network profiles are never complete. Ascertaining personality traits and accurate determination of age and gender can make such social networks safer and stronger instruments of true collaboration and human connection. In my talk, I will discuss the predictive models we built with the Social Media group within the Center for Data Science to infer users’ missing or misrepresented attributes. I will also demonstrate how our models use a complex set of links in social networks to predict age and gender accurately. Our model uses publicly available data to find privately accurate information about a user in aid of law enforcement, communal safety and social well-being

Scalable adaptive label propagation in Grappa

Nodes of a social graph often represent entities with specific labels, denoting properties such as age-group or gender. Design of algorithms to assign labels to unlabeled nodes by leveraging node-proximity and a-priori labels of seed nodes is of significant interest. A semi-supervised approach to solve this problem is termed "LPA-Label Propagation Algorithm" where labels of a subset of nodes are iteratively propagated through the network to infer yet unknown node labels. While LPA for node labelling is extremely fast and simple, it works well only with an assumption of node-homophily - connected nodes are connected because they must deserve a similar label - which can often be a misnomer. In this paper we propose a novel algorithm "Adaptive Label Propagation" that dynamically adapts to the underlying characteristics of homophily, heterophily, or otherwise, of the connections of the network, and applies suitable label propagation strategies accordingly. Moreover, our adaptive label propagation approach is scalable as demonstrated by its implementation in Grappa, a distributed shared-memory system.Our experiments on social graphs from Facebook, YouTube, Live Journal, Orkut and Netlog demonstrate that our approach not only improves the labelling accuracy but also computes results for millions of users within a few seconds.status: publishe

A Checklist for Assessing the Methodological Quality of Concurrent tES-fMRI Studies (ContES Checklist): A Consensus Study and Statement

International audienceBackground Low intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation (tACS or tDCS), applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional magnetic resonance imaging (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies.Objective: To develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency, and reproducibility (ContES Checklist).Methods: A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists (EP) through the International Network of the tES-fMRI (INTF) Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC based on a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed using the checklist.Results Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (1) technological factors, (2) safety and noise tests, and (3) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article.Conclusions Use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies, and increase methodological transparency and reproducibility