1,374 research outputs found
Entropy-based randomisation of rating networks
In the last years, due to the great diffusion of e-commerce, online rating
platforms quickly became a common tool for purchase recommendations. However,
instruments for their analysis did not evolve at the same speed. Indeed,
interesting information about users' habits and tastes can be recovered just
considering the bipartite network of users and products, in which links have
different weights due to the score assigned to items. With respect to other
weighted bipartite networks, in these systems we observe a maximum possible
weight per link, that limits the variability of the outcomes. In the present
article we propose an entropy-based randomisation of (bipartite) rating
networks by extending the Configuration Model framework: the randomised network
satisfies the constraints of the degree per rating, i.e. the number of given
ratings received by the specified product or assigned by the single user. We
first show that such a null model is able to reproduce several non-trivial
features of the real network better than other null models. Then, using it as a
benchmark, we project the information contained in the real system on one of
the layers, showing, for instance, the division in communities of music albums
due to the taste of customers, or, in movies due the audience.Comment: 12 pages, 30 figure
Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin
What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Explainable AI (XAI) is a rapidly evolving field that aims to improve
transparency and trustworthiness of AI systems to humans. One of the unsolved
challenges in XAI is estimating the performance of these explanation methods
for neural networks, which has resulted in numerous competing metrics with
little to no indication of which one is to be preferred. In this paper, to
identify the most reliable evaluation method in a given explainability context,
we propose MetaQuantus -- a simple yet powerful framework that meta-evaluates
two complementary performance characteristics of an evaluation method: its
resilience to noise and reactivity to randomness. We demonstrate the
effectiveness of our framework through a series of experiments, targeting
various open questions in XAI, such as the selection of explanation methods and
optimisation of hyperparameters of a given metric. We release our work under an
open-source license to serve as a development tool for XAI researchers and
Machine Learning (ML) practitioners to verify and benchmark newly constructed
metrics (i.e., ``estimators'' of explanation quality). With this work, we
provide clear and theoretically-grounded guidance for building reliable
evaluation methods, thus facilitating standardisation and reproducibility in
the field of XAI.Comment: 30 pages, 12 figures, 3 table
GOSSIPKIT: A Unified Component Framework for Gossip
International audienceAlthough the principles of gossip protocols are relatively easy to grasp, their variety can make their design and evaluation highly time consuming. This problem is compounded by the lack of a unified programming framework for gossip, which means developers cannot easily reuse, compose, or adapt existing solutions to fit their needs, and have limited opportunities to share knowledge and ideas. In this paper, we consider how component frameworks, which have been widely applied to implement middleware solutions, can facilitate the development of gossip-based systems in a way that is both generic and simple. We show how such an approach can maximise code reuse, simplify the implementation of gossip protocols, and facilitate dynamic evolution and re-deployment
Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position
Videos and commercials produced for large audiences can elicit mixed opinions. We wondered whether this diversity is also reflected in the way individuals watch the videos. To answer this question, we presented 65 commercials with high production value to 25 individuals while recording their eye movements, and asked them to provide preference ratings for each video. We find that gaze positions for the most popular videos are highly correlated. To explain the correlations of eye movements, we model them as ÂȘinteractionsÂș between individuals. A thermodynamic analysis of these interactions shows that they approach a ÂȘcritical Âș point such that any stronger interaction would put all viewers into lock-step and any weaker interaction would fully randomise patterns. At this critical point, groups with similar collective behaviour in viewing patterns emerge while maintaining diversity between groups. Our results suggest that popularity of videos is already evident in the way we look at them, and that we maintain diversity in viewing behaviour even as distinct patterns of groups emerge. Our results can be used to predict popularity of videos and commercials at the population level from the collective behaviour of the eye movements of a few viewers
Subjective interestingness of subgraph patterns
The utility of a dense subgraph in gaining a better understanding of a graph has been formalised in numerous ways, each striking a different balance between approximating actual interestingness and computational efficiency. A difficulty in making this trade-off is that, while computational cost of an algorithm is relatively well-defined, a pattern's interestingness is fundamentally subjective. This means that this latter aspect is often treated only informally or neglected, and instead some form of density is used as a proxy. We resolve this difficulty by formalising what makes a dense subgraph pattern interesting to a given user. Unsurprisingly, the resulting measure is dependent on the prior beliefs of the user about the graph. For concreteness, in this paper we consider two cases: one case where the user only has a belief about the overall density of the graph, and another case where the user has prior beliefs about the degrees of the vertices. Furthermore, we illustrate how the resulting interestingness measure is different from previous proposals. We also propose effective exact and approximate algorithms for mining the most interesting dense subgraph according to the proposed measure. Usefully, the proposed interestingness measure and approach lend themselves well to iterative dense subgraph discovery. Contrary to most existing approaches, our method naturally allows subsequently found patterns to be overlapping. The empirical evaluation highlights the properties of the new interestingness measure given different prior belief sets, and our approach's ability to find interesting subgraphs that other methods are unable to find
Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections
According to the Eurobarometer report about EU media use of May 2018, the
number of European citizens who consult on-line social networks for accessing
information is considerably increasing. In this work we analyze approximately
tweets exchanged during the last Italian elections. By using an
entropy-based null model discounting the activity of the users, we first
identify potential political alliances within the group of verified accounts:
if two verified users are retweeted more than expected by the non-verified
ones, they are likely to be related. Then, we derive the users' affiliation to
a coalition measuring the polarization of unverified accounts. Finally, we
study the bipartite directed representation of the tweets and retweets network,
in which tweets and users are collected on the two layers. Users with the
highest out-degree identify the most popular ones, whereas highest out-degree
posts are the most "viral". We identify significant content spreaders by
statistically validating the connections that cannot be explained by users'
tweeting activity and posts' virality by using an entropy-based null model as
benchmark. The analysis of the directed network of validated retweets reveals
signals of the alliances formed after the elections, highlighting commonalities
of interests before the event of the national elections
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