An Integrated Polysaccharide Hydrogel with Versatile Fluorescence Responses through Noncovalent Reformation of Gel Aggregation and for Bioimaging.

Functionalized hydrogels, with their unique and adaptable structures, have attracted significant attention in materials and biomaterials research. Fluorescent hydrogels are particularly noteworthy for their sensing capabilities and ability to mimic cellular matrices, facilitating cell infiltration and tracking of drug delivery. Structural elucidation of hydrogels is crucial for understanding their responses to stimuli such as the pH, temperature, and solvents. This study developed a fluorescent hydrogel by functionalizing chitosan with p-cresol-based quinazolinone aldehyde. Confocal microscopy revealed the hydrogel's intriguing fluorogenic properties. The hydrogel exhibited enhanced fluorescence and a tunable network morphology, influenced by the THF-water ratio. The study investigated the control of gel network reformation in different media and analyzed the fluorescence responses and structural changes of the sugar backbone and fluorophore. Proper selection of mixed solvents is essential for optimizing the hydrogel as a fluorescence probe for bioimaging. This hydrogel demonstrated greater swelling properties, making it highly suitable for drug delivery applications.

On the rise of fear speech in online social media.

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

Constant community identification in million-scale networks.

The inherently stochastic nature of community detection in real-world complex networks poses an important challenge in assessing the accuracy of the results. In order to eliminate the algorithmic and implementation artifacts, it is necessary to identify the groups of vertices that are always clustered together, independent of the community detection algorithm used. Such groups of vertices are called constant communities. Current approaches for finding constant communities are very expensive and do not scale to large networks. In this paper, we use binary edge classification to find constant communities. The key idea is to classify edges based on whether they form a constant community or not. We present two methods for edge classification. The first is a GCN-based semi-supervised approach that we term Line-GCN. The second is an unsupervised approach based on image thresholding methods. Neither of these methods requires explicit detection of communities and can thus scale to very large networks of the order of millions of vertices. Both of our semi-supervised and unsupervised results on real-world graphs demonstrate that the constant communities obtained by our method have higher F1-scores and comparable or higher NMI scores than other state-of-the-art baseline methods for constant community detection. While the training step of Line-GCN can be expensive, the unsupervised algorithm is 10 times faster than the baseline methods. For larger networks, the baseline methods cannot complete, whereas all of our algorithms can find constant communities in a reasonable amount of time. Finally, we also demonstrate that our methods are robust under noisy conditions. We use three different, well-studied noise models to add noise to the networks and show that our results are mostly stable.

Unsupervised ranking of clustering algorithms by INFOMAX.

Clustering and community detection provide a concise way of extracting meaningful information from large datasets. An ever growing plethora of data clustering and community detection algorithms have been proposed. In this paper, we address the question of ranking the performance of clustering algorithms for a given dataset. We show that, for hard clustering and community detection, Linsker's Infomax principle can be used to rank clustering algorithms. In brief, the algorithm that yields the highest value of the entropy of the partition, for a given number of clusters, is the best one. We show indeed, on a wide range of datasets of various sizes and topological structures, that the ranking provided by the entropy of the partition over a variety of partitioning algorithms is strongly correlated with the overlap with a ground truth partition The codes related to the project are available in https://github.com/Sandipan99/Ranking_cluster_algorithms.

Epidemic spreading through direct and indirect interactions.

In this paper we study the susceptible-infected-susceptible epidemic dynamics, considering a specialized setting where popular places (termed passive entities) are visited by agents (termed active entities). We consider two types of spreading dynamics: direct spreading, where the active entities infect each other while visiting the passive entities, and indirect spreading, where the passive entities act as carriers and the infection is spread via them. We investigate in particular the effect of selection strategy, i.e., the way passive entities are chosen, in the spread of epidemics. We introduce a mathematical framework to study the effect of an arbitrary selection strategy and derive formulas for prevalence, extinction probabilities, and epidemic thresholds for both indirect and direct spreading. We also obtain a very simple relationship between the extinction probability and the prevalence. We pay special attention to preferential selection and derive exact formulas. The analysis reveals that an increase in the diversity in the selection process lowers the epidemic thresholds. Comparing the direct and indirect spreading, we identify regions in the parameter space where the prevalence of the indirect spreading is higher than the direct one.

Intergroup networks as random threshold graphs.

Similar-minded people tend to form social groups. Due to pluralistic homophily as well as a sort of heterophily, people also participate in a wide variety of groups. Thus, these groups generally overlap with each other; an overlap between two groups can be characterized by the number of common members. These common members can play a crucial role in the transmission of information between the groups. As a step towards understanding the information dissemination, we perceive the system as a pruned intergroup network and show that it maps to a very basic graph theoretic concept known as a threshold graph. We analyze several structural properties of this network such as degree distribution, largest component size, edge density, and local clustering coefficient. We compare the theoretical predictions with the results obtained from several online social networks (LiveJournal, Flickr, YouTube) and find a good match.

Information sharing and sorting in a community.

We present the results of a detailed numerical study of a model for the sharing and sorting of information in a community consisting of a large number of agents. The information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information. Although our model is less restricted compared to the well-established naming game, the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume nontrivial values. Finally, it appears that in analogy to the emergence of clusters in the phenomenon of percolation, one can define clusters of agents here having the same information. We have studied in detail the growth of the largest cluster in this article and performed its finite-size scaling analysis.

Constant communities in complex networks.

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

Opinion formation in time-varying social networks: The case of the naming game.

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

On the origin of the hierarchy of color names.

One of the fundamental problems in cognitive science is how humans categorize the visible color spectrum. The empirical evidence of the existence of universal or recurrent patterns in color naming across cultures is paralleled by the observation that color names begin to be used by individual cultures in a relatively fixed order. The origin of this hierarchy is largely unexplained. Here we resort to multiagent simulations, where a population of individuals, subject to a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference, categorizes and names colors through a purely cultural negotiation in the form of language games. We found that the time needed for a population to reach consensus on a color name depends on the region of the visible color spectrum. If color spectrum regions are ranked according to this criterion, a hierarchy with [red, (magenta)-red], [violet], [green/yellow], [blue], [orange], and [cyan], appearing in this order, is recovered, featuring an excellent quantitative agreement with the empirical observations of the WCS. Our results demonstrate a clear possible route to the emergence of hierarchical color categories, confirming that the theoretical modeling in this area has now attained the required maturity to make significant contributions to the ongoing debates concerning language universals.

Aging in language dynamics.

Human languages evolve continuously, and a puzzling problem is how to reconcile the apparent robustness of most of the deep linguistic structures we use with the evidence that they undergo possibly slow, yet ceaseless, changes. Is the state in which we observe languages today closer to what would be a dynamical attractor with statistically stationary properties or rather closer to a non-steady state slowly evolving in time? Here we address this question in the framework of the emergence of shared linguistic categories in a population of individuals interacting through language games. The observed emerging asymptotic categorization, which has been previously tested--with success--against experimental data from human languages, corresponds to a metastable state where global shifts are always possible but progressively more unlikely and the response properties depend on the age of the system. This aging mechanism exhibits striking quantitative analogies to what is observed in the statistical mechanics of glassy systems. We argue that this can be a general scenario in language dynamics where shared linguistic conventions would not emerge as attractors, but rather as metastable states.

Modeling discrete combinatorial systems as alphabetic bipartite networks: theory and applications.

Genes and human languages are discrete combinatorial systems (DCSs), in which the basic building blocks are finite sets of elementary units: nucleotides or codons in a DNA sequence, and letters or words in a language. Different combinations of these finite units give rise to potentially infinite numbers of genes or sentences. This type of DCSs can be represented as an alphabetic bipartite network (ABN) where there are two kinds of nodes, one type represents the elementary units while the other type represents their combinations. Here, we extend and generalize recent analytical findings for ABNs derived in [Peruani, Europhys. Lett. 79, 28001 (2007)] and empirically investigate two real world systems in terms of ABNs, the codon gene and the phoneme-language network. The one-mode projections onto the elementary basic units are also studied theoretically as well as in real world ABNs. We propose the use of ABNs as a means for inferring the mechanisms underlying the growth of real world DCSs.

SweepSticks: an adaptive virtual mouse for people with neuromotor disorders.

SweepSticks has been developed primarily to provide alternative mouse access to people with neuromotor disorders, especially those suffering from cerebral palsy. This tool enables the user to perform both mouse movements and clicks emulated by a software interface, which is controlled by some special hardware switches. It is also capable of adapting itself to the behavior of the user, which it does by tracing and recording the sequence of her or his mouse actions and subsequently providing relevant suggestions to her or him in the future. The field experiments carried out with real users suggest that the tool may be quite effective in serving most of the computer access needs of the user.