TwitterMancer: Predicting Interactions on Twitter Accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/. Keywords: Graph mining, machine learning, social media, social network

TwitterMancer: predicting interactions on Twitter accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions.We have crawled an extensive data set of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain “higher-dimensional” triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/.Accepted manuscrip

Densest Diverse Subgraphs: How to Plan a Successful Cocktail Party with Diversity

Dense subgraph discovery methods are routinely used in a variety of applications including the identification of a team of skilled individuals for collaboration from a social network. However, when the network's node set is associated with a sensitive attribute such as race, gender, religion, or political opinion, the lack of diversity can lead to lawsuits. In this work, we focus on the problem of finding a densest diverse subgraph in a graph whose nodes have different attribute values/types that we refer to as colors. We propose two novel formulations motivated by different realistic scenarios. Our first formulation, called the densest diverse subgraph problem (DDSP), guarantees that no color represents more than some fraction of the nodes in the output subgraph, which generalizes the state-of-the-art due to Anagnostopoulos et al. (CIKM 2020). By varying the fraction we can range the diversity constraint and interpolate from a diverse dense subgraph where all colors have to be equally represented to an unconstrained dense subgraph. We design a scalable $\Omega(1/\sqrt{n})$-approximation algorithm, where $n$ is the number of nodes. Our second formulation is motivated by the setting where any specified color should not be overlooked. We propose the densest at-least-$\vec{k}$-subgraph problem (Dal$\vec{k}$S), a novel generalization of the classic Dal$k$S, where instead of a single value $k$, we have a vector ${\mathbf k}$ of cardinality demands with one coordinate per color class. We design a $1/3$-approximation algorithm using linear programming together with an acceleration technique. Computational experiments using synthetic and real-world datasets demonstrate that our proposed algorithms are effective in extracting dense diverse clusters.Comment: Accepted to KDD 202

ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach

Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world problems can be cast as graph inference tasks where the graph representation could capture complex relational phenomena (e.g., transactions among financial accounts in a journal entry), along with metadata reflecting tabular features (e.g. approver, effective date, etc.). While numerous anomaly detectors based on Graph Neural Networks (GNNs) have been proposed, none are capable of directly handling directed graphs with multi-edges and self-loops. Furthermore, the simultaneous handling of relational and tabular features remains an unexplored area. In this work we propose ADAMM, a novel graph neural network model that handles directed multi-graphs, providing a unified end-to-end architecture that fuses metadata and graph-level representation learning through an unsupervised anomaly detection objective. Experiments on datasets from two different domains, namely, general-ledger journal entries from different firms (accounting) as well as human GPS trajectories from thousands of individuals (urban mobility) validate ADAMM's generality and detection effectiveness of expert-guided and ground-truth anomalies. Notably, ADAMM outperforms existing baselines that handle the two data modalities (graph and metadata) separately with post hoc synthesis efforts.Comment: Accepted at IEEE BigData 202

Current trends of the serve skill in relation to the in-game roles of the elite volleyball players: Comparison between genders

The aim of this study was to investigate the current trends of the serve skill for each one of the in-game roles of the players for both genders in high-level volleyball. The serve actions of male (M) and female (F) elite players from 20 volleyball games (M = 10, F = 10) of the final phase World League 2018 were assessed. The analysed variables were comprised of the serve type, the serving area (SA), the serve direction, the in-game role of the server and the serve performance. Results showed that men mainly preferred the power jump serve from the areas behind zones 1 (SA1) and 6 (SA6), while they directed them to the back of the court and made more mistakes than women. Women preferred the floating jump serve directed to the left central part of the court. Regarding the in-game roles of the men, the receivers-attackers and the middle-attackers carried out their serves from SA1 and SA6 more frequently compared to women who preferred to use the serve area behind zone 5 (SA5) more frequently than men. Additionally, women opposites and setters chose to use the SA1 more frequently and the SA5 and SA6 less frequently in comparison to their men counterparts

Scattering investigation of multiscale organization in aqueous solutions of native xanthan

The hierarchical morphology of xanthan solutions is analyzed by light and neutron scattering in a broad range of concentrations in order to connect their morphology to their well-documented dynamic properties. Static light scattering inside the semidilute regime is dominated by the form factor of individual xanthan chains while at higher concentrations chain interconnections appear to modify the low wave vector scattering. Dynamic light scattering reveals the self-similar nature of the solutions caused by interchain associations as intensity autocorrelation functions present power-law behaviour. Small angle neutron scattering is dominated by the fractal scattering from the formed network at intermediate length scales. At small length scales the rigid structure of xanthan is revealed and the molecular weight per unit length is extracted. No detectable morphological alterations for shear rates up to 1000 rad/s are observed revealing that the shear thinning behaviour of xanthan is related to the disruption of chain-chain associations

COMPARISON AND ASSESSMENT OF THE SETTING ZONE CHOICES BY ELITE MALE AND FEMALE VOLLEYBALL SETTERS IN RELATION TO THE QUALITY OF DEFENCE

The aim of this study was the assessment and comparison of the setting zone choices by male and female elite setters, in relation to the quality of the defensive actions which were carried out in Complex II (KII) and III (KIII). A three-member group of coaches assessed the actions of male (M) and female (F) setters and defenders from 20 volleyball games (M=10, F=10) of National Teams competing in the final phase of the World League 2017. The assessment was based on a 5-point rating scale and included actions that composed a set of 2 contacts in KII and KIII. The test of independence for the variables (“setting zones”, “defense quality”) was carried out using Fisher's exact test. Following the overall independence test we tested the difference in proportions between genders for each level of the “setting zone” variable. Results showed that in KII the differences in proportions between genders for each level of the “setting zone” variable were found for zone 4 in favor of the male and zones 2 and 6 in favor of the female players. In KIII and under excellent defensive actions the difference in proportions between genders was found in zones 1 and 6 in favor of the males. In conclusion, under suboptimal and optimal conditions, male setters set the ball to zones 4, 6 and 1 more frequently than females, incorporating them into their offensive strategy, while the latter under good conditions preferred setting to zones 2 and 6