GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports

Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games

Las mujeres sostienen (más de) la mitad del cielo: examinando las motivaciones, los comportamientos y el capital social en un juego multijugador popular entre las jugadoras

This study combined survey and behavioral data to examine the connections between socio-relational motivations, socializing behaviors, and social capital. Participants were U.S. players (N = 1,027; 65% female) from a fantasy mobile game popular among female players. Consistent with gender role theory, female players partook in higher socializing behaviors. Moreover, in support of uses and gratifications theory and the social capital framework, the smarty-pants and socializing motivations, and socializing behaviors were positively associated with social capital. Partly in support of gender role theory and the social capital framework, both bridging and bonding social capital were higher for female players. This study’s results offer nuance to how certain game affordances and incentives may be predictive of social capital outcomes.Este estudio combina datos encuestas y comportamientais para examinar las conexiones entre las motivaciones sociorrelacionales, los comportamientos de socialización y el capital social del juego. Las personas que participaron fueron jugadores estadounidenses (N = 1.027; 65 % mujeres) del un juego de fantasía popular entre las jugadoras. En consonancia con la teoría de los roles de género, las jugadoras participaron en mayores comportamientos de socialización. Además, siguiendo la teoría de los usos y gratificaciones y la teoría del capital social, las motivaciones de los sabelotodo y las socializadoras, así como los comportamientos de socialización, están positivamente asociados con el capital social. Confirmando parcialmente los roles de género y capital social, tanto el capital social puente como el vínculo fue mayor entre las jugadoras. Los resultados de este estudio ofrecen un matiz sobre la forma en que ciertos propósitos e incentivos del juego pueden predecir los resultados del capital social

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

SCOPE: Selective Cross-Validation over Parameters for Elo

It is crucial to develop reusable methods for explaining and evaluating esports given their popularity and diversity. Quantifying skill in an esport has the potential to improve win prediction, matchmaking, and storytelling for these games. Arpad Elo’s skill modeling system for chess has been adapted to many games and sports. In each instance, the modeler is challenged with tuning parameters to optimize for some metric, usually accuracy. Often these approaches are one-off and lack consistency. We propose SCOPE, a framework that uses grid search cross-validation to select optimal parameters for Elo based on accuracy, calibration, or log loss. We demonstrate this method on a season of Call of Duty World League, a first-person shooter esport, and we demonstrate comparable performance to other more complex, state-of-the-art methods

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense