DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table

Statistically Significant Detection of Linguistic Change

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.Comment: 11 pages, 7 figures, 4 table

Consumer Adoption of Self-Service Technologies in the Context of the Jordanian Banking Industry: Examining the Moderating Role of Channel Types

YesThis study aimed to examine the key factors predicting Jordanian consumers’ intentions and usage of three types of self-service banking technologies. This study also sought to test if the impacts of these main predictors could be moderated by channel type. This study proposed a conceptual model by integrating factors from the unified theory of acceptance and use of technology (UTAUT), along with perceived risk. The required data were collected from a convenience sample of Jordanian banking customers using a survey questionnaire. The statistical results strongly support the significant influence of performance expectancy, social influence, and perceived risk on customer intentions for the three types of SSTs examined. The results of the X2 differences test also indicate that there are significant differences in the influence of the main predictors due to the moderating effect of channel type. One of the key contributions of this study is that three types of SSTs were tested in a single study, which had not been done before, leading to the identification of the factors common to all three types, as well as the salient factors unique to each type

Jugendliche im laendlichen Raum Ergebnisse einer Repraesentativumfrage im Odenwaldkreis. Bd. 1: Berichtsband

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