1,720,963 research outputs found
Generating realistic interest-driven information cascades
Full text linkWe propose a model for the synthetic generation of information cascades in social media. In our model the information “memes” propagating in the social network are characterized by a probability distribution in a topic space, accompanied by a textual description, i.e., a bag of keywords coherent with the topic distribution. Similarly, every user of the social media is described by a vector of interests defined over the same topic space. Information cascades are governed by the topic of the meme, its level of virality, the interests of each user, community pressure, and social influence.
The main technical challenge we face towards our goal is the generation of realistic interest vectors, given a known network structure and a tunable level of homophily. We tackle this problem by means of a method based on non-negative matrix factorization, which is shown experimentally to outperform non-trivial baselines based on label propagation and random-walk-based graph embedding.
As we showcase in our experiments, our model offers a small set of simple and easily interpretable “knobs” which allow to study, in vitro, how each set of assumptions affects the resulting propagations. Finally, we show how to generate synthetic cascades that have similar macro-statistics to the real world cascades for a dataset containing both the network and the cascades
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
No full textImages are loaded with semantic information that pertains to real-world ontologies: dog breeds share mammalian similarities, food pictures are often depicted in domestic environments, and so on. However, when training machine learning models for image classification, the relative similarities amongst object classes are commonly paired with one-hot-encoded labels. According to this logic, if an image is labelled as spoon, then tea-spoon and shark are equally wrong in terms of training loss. To overcome this limitation, we explore the integration of additional goals that reflect ontological and semantic knowledge, improving model interpretability and trustworthiness. We suggest a generic approach that allows to derive an additional loss term starting from any kind of semantic information about the classification label. First, we show how to apply our approach to ontologies and word embeddings, and discuss how the resulting information can drive a supervised learning process. Second, we use our semantically enriched loss to train image classifiers, and analyse the trade-offs between accuracy, mistake severity, and learned internal representations. Finally, we discuss how this approach can be further exploited in terms of explainability and adversarial robustness
Time-varying social networks in a graph database: [A Neo4j use case]
No full textRepresenting and efficiently querying time-varying social network data is a central challenge that needs to be addressed in order to support a variety of emerging applications that leverage high-resolution records of human activities and interactions from mobile devices and wearable sensors. In order to support the needs of specific applications, as well as general tasks related to data curation, cleaning, linking, post-processing, and data analysis, data models and data stores are needed that afford efficient and scalable querying of the data. In particular, it is important to design solutions that allow rich queries that simultaneously involve the topology of the social network, temporal information on the presence and interactions of individual nodes, and node metadata. Here we introduce a data model for time-varying social network data that can be represented as a property graph in the Neo4j graph database. We use time-varying social network data collected by using wearable sensors and study the performance of real-world queries, pointing to strengths, weaknesses and challenges of the proposed approach. Copyright © 2013 ACM
Detecting anomalies in time-varying networks using tensor decomposition
No full textNew data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as time-varying networks. This brings forth new challenges for the identification and removal of time-varying graph anomalies that entail complex correlations of topological features and temporal activity patterns. Here we present an anomaly detection approach for temporal graph data, based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable proximity sensors. The dataset includes metadata that allow to independently build a ground truth, used to validate the anomaly detection method. Our approach achieves high accuracy in identifying meso-scale network anomalies due to sensor wearing protocol, proving the practical viability of the method for a real-world application
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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