Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics.

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

Dye-Sensitized Fe-MOF nanosheets as Visible-Light driven photocatalyst for high efficient photocatalytic CO2 reduction.

Dye-sensitized system holds great potential for the development of visible-light-responsive photocatalysts not only because it can enhance the light absorption and charge separation efficiency of the systems but also because it can tune the band structure of catalysts. Herein, two-dimensional (2D) Fe-MOF nanosheets (Fe-MNS) with a LUMO potential of 0.11 V (vs. RHE) was prepared. Interestingly, it has been found that when the 2D Fe-MNS catalyst was functionalized with visible-light-responsive [Ru(bpy)]32+ as a dye-sensitizer, the electrons from the [Ru(bpy)]32+ can effectively inject into the 2D Fe-MNS, which resulted in a negative shift of the LUMO potential of the 2D Fe-MNS to -0.15 V (vs. RHE). Consequently, the [Ru(bpy)]32+/Fe-MNS catalytic system exhibits a sound photocatalytic CO2-to-CO activity of 1120 µmol g-1h-1 under visible-light-irradiation. The photocatalytic CO production was further ameliorated by regulating the electronic structure of the 2D Fe-MNS by doping Co ions, achieving a remarkable photocatalytic activity of 1637 µmol g-1h-1. This work further supports that the dye-sensitized system is an auspicious strategy worth exploring with different catalysts for the development of visible-light-responsive photocatalytic systems.

Discovering latent node Information by graph attention network.

In this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

MOF-derived synthesis of MnS/In2S3p-n heterojunctions with hierarchical structures for efficient photocatalytic CO2 reduction.

Photoreduction of CO2 to valuable fuels with semiconductor photocatalysts is a good solution to the problems of global warming and energy crisis. Creation of hybrid nanomaterials with hierarchical and/or heterojunction structures is beneficial to develop efficient photocatalysts for CO2 reduction. Herein we present a convenient method to obtain a hybrid photocatalyst consisting of MnS and In2S3 nanosheets with assembled hierarchical structures through using Mn2+-loaded MIL-68(In) submicro-rods as templates. Owing to the dispersive Mn2+ and In3+ ions in templates, numerous small p-n heterojunctions of MnS/In2S3 could be simultaneously produced in each hierarchical particle. The p-type MnS and n-type In2S3 with an original type II band alignment can create a stronger built-in electric field after the formation of p-n heterojunctions, which is favorable for charge separation and migration to catalyst surface. The prepared MnS/In2S3 heterojunctions show an 4-fold higher photocatalytic activity toward CO2 reduction than pristine MnS and In2S3. The MnS/In2S3 hierarchical structures were well characterized and their working mechanism was explored. This work demonstrates a facile strategy to create efficient hybrid photocatalysts with both hierarchical structures and p-n heterojunctions for photocatalytic applications.

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows.

The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users' collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What's more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other.