Quantifying Triadic Closure in Multi-Edge Social Networks

Multi-edge networks capture repeated interactions between individuals. In social networks, such edges often form closed triangles, or triads. Standard approaches to measure this triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks of social interactions based on a shared partner statistic. We demonstrate that our operalization is able to detect meaningful closure in synthetic and empirical multi-edge networks, where common approaches fail. This is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table

How visual cues to speech rate influence speech perception

Spoken words are highly variable and therefore listeners interpret speech sounds relative to the surrounding acoustic context, such as the speech rate of a preceding sentence. For instance, a vowel midway between short /ɑ/ and long /a:/ in Dutch is perceived as short /ɑ/ in the context of preceding slow speech, but as long /a:/ if preceded by a fast context. Despite the well-established influence of visual articulatory cues on speech comprehension, it remains unclear whether visual cues to speech rate also influence subsequent spoken word recognition. In two ‘Go Fish’-like experiments, participants were presented with audio-only (auditory speech + fixation cross), visual-only (mute videos of talking head), and audiovisual (speech + videos) context sentences, followed by ambiguous target words containing vowels midway between short /ɑ/ and long /a:/. In Experiment 1, target words were always presented auditorily, without visual articulatory cues. Although the audio-only and audiovisual contexts induced a rate effect (i.e., more long /a:/ responses after fast contexts), the visual-only condition did not. When, in Experiment 2, target words were presented audiovisually, rate effects were observed in all three conditions, including visual-only. This suggests that visual cues to speech rate in a context sentence influence the perception of following visual target cues (e.g., duration of lip aperture), which at an audiovisual integration stage bias participants’ target categorization responses. These findings contribute to a better understanding of how what we see influences what we hear

Target absent trials in configural contextual cuing

In contextual cueing (CC), reaction times to find targets in repeated displays are faster than in displays that have never been seen before. This has been demonstrated using target-distractor configurations, global background colors, naturalistic scenes and the co-variation of target with distractors. The majority of CC studies have used displays where the target is always present. This paper investigates what happens when the target is sometimes absent. Experiment 1 shows that, although configural CC occurs in displays when the target is always present, there is no CC when the target is always absent. Experiment 2 shows that there is no CC when the same spatial layout can be both target present and target absent on different trials. The presence of distractors in locations that contain targets on other trials appears to interfere with CC and even disrupts the expression of previously learned contexts (Experiments 3-5). The results show that it is the target-distractor associations that are important in producing CC and, consistent with a response selection account, changing the response type from an orientation task to a detection task removes the CC effect

Space-efficient detection of unusual words

Detecting all the strings that occur in a text more frequently or less frequently than expected according to an IID or a Markov model is a basic problem in string mining, yet current algorithms are based on data structures that are either space-inefficient or incur large slowdowns, and current implementations cannot scale to genomes or metagenomes in practice. In this paper we engineer an algorithm based on the suffix tree of a string to use just a small data structure built on the Burrows-Wheeler transform, and a stack of $O(\sigma^2\log^2 n)$ bits, where $n$ is the length of the string and $\sigma$ is the size of the alphabet. The size of the stack is $o(n)$ except for very large values of $\sigma$. We further improve the algorithm by removing its time dependency on $\sigma$, by reporting only a subset of the maximal repeats and of the minimal rare words of the string, and by detecting and scoring candidate under-represented strings that $\textit{do not occur}$ in the string. Our algorithms are practical and work directly on the BWT, thus they can be immediately applied to a number of existing datasets that are available in this form, returning this string mining problem to a manageable scale.Comment: arXiv admin note: text overlap with arXiv:1502.0637