Person Knowledge Is Independently Encoded by Allocentric and Egocentric Reference Frames Within Separate Brain Systems

Review for "Person Knowledge Is Independently Encoded by Allocentric and Egocentric Reference Frames Within Separate Brain Systems"

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21\(\times \) speedup in execution time while maintaining matching performance in terms of influence spread.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.

As we move through an environment, the positions of surrounding objects relative to our body constantly change, with some objects even leaving our field of view. As a consequence, maintaining orientation requires spatial updating, the continuous monitoring of self-motion cues to update external locations within an egocentric frame of reference. While previous research using functional magnetic resonance imaging has implicated the precuneus in spatial updating, direct evidence for this claim is missing. To address this important question, we applied theta burst repetitive transcranial magnetic stimulation (rTMS) over the precuneus to induce a “virtual lesion”. Following stimulation, participants were tested in a large-scale virtual environment in which they had to use visual self-motion information to keep track of the position of virtual objects. Compared to sham stimulation, rTMS affected working memory traces for object locations. Critically, rTMS further impaired the ability to update these locations whenever participants experienced simulated movement. As this effect could not be explained by working memory deficits alone, we conclude that visual spatial updating relies on the construction of updated representations of egocentric object locations within the precuneus. Together, these findings establish the precuneus as performing key computations for the formation of cognitive maps.

A key challenge in the social network community is the problem of network generation -- that is, how can we create synthetic networks that match characteristics traditionally found in most real world networks? Important characteristics that are present in social networks include a power law degree distribution, small diameter, and large amounts of clustering. However, most current network generators, such as the Chung Lu and Kronecker models, largely ignore the clustering present in a graph and focus on preserving other network statistics, such as the power law distribution. Models such as the exponential random graph model have a transitivity parameter that can capture clustering, but they are computationally difficult to learn, making scaling to large real world networks intractable. In this work, we propose an extension to the Chung Lu random graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion transitive edges. Specifically, it combines the standard Chung Lu model with edges that are formed through transitive closure (e.g., by connecting a 'friend of a friend'). We prove TCL's expected degree distribution is equal to the degree distribution of the original input graph, while still providing the ability to capture the clustering in the network. The single parameter required by our model can be learned in seconds on graphs with millions of edges, networks can be generated in time that is linear in the number of edges. We demonstrate the performance of TCL on four real-world social networks, including an email dataset with hundreds of thousands of nodes and millions of edges, showing TCL generates graphs that match the degree distribution, clustering coefficients and hop plots of the original networks.

Background Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. Purpose The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. Methods We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. Results The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.

Multiple pathway models of attention deficit hyperactivity disorder (ADHD) suggest that this disorder is the behavioural expression of dysfunction in one of several separable brain systems. One such model focuses on the brain systems underlying cognitive control, timing and reward sensitivity. It predicts separable subgroups among individuals with ADHD, with performance deficits in only one of these domains. We used latent class analysis (LCA) to identify subgroups of individuals with ADHD based on their overall pattern of neuropsychological performance, rather than grouping them based on cut-off criteria. We hypothesized that we would find separable subgroups with deficits in cognitive control, timing and reward sensitivity respectively. Ninety-six subjects with ADHD (of any subtype) and 121 typically developing controls performed a battery assessing cognitive control, timing and reward sensitivity. LCA was used to identify subgroups of individuals with ADHD with a distinct neuropsychological profile. A similar analysis was performed for controls. Three subgroups represented 87% of subjects with ADHD. Two of our three hypothesized subgroups were identified, with poor cognitive control and timing. Two of the ADHD subgroups had similar profiles to control subgroups, whereas one subgroup had no equivalent in controls. Our findings support multiple pathway models of ADHD, as we were able to define separable subgroups with differing cognitive profiles. Furthermore, we found both quantitative and qualitative differences from controls, suggesting that ADHD may represent both categorical and dimensional differences. These results show that by addressing heterogeneity in ADHD, we can identify more homogeneous subsets of individuals to further investigate.

Sex differences in navigation have often been attributed to the use of different navigation strategies in men and women. However, no study so far has investigated sex differences in the brain networks supporting different navigation strategies. To address this issue, we employed a 3D-navigation task during functional MRI in 36 men and 36 women, all scanned thrice, and modeled navigation strategies by instructions requiring an allocentric vs. egocentric reference frame on the one hand, as well as landmark-based vs. Euclidian strategies on the other hand. We found distinct brain networks supporting different perspectives/strategies. Men showed stronger activation of frontal areas, whereas women showed stronger activation of posterior brain regions. The left inferior frontal gyrus was more strongly recruited during landmark-based navigation in men. The hippocampus showed stronger connectivity with left-lateralized frontal areas in women and stronger connectivity with superior parietal areas in men. We discuss these findings in the light of a stronger recruitment of verbal networks supporting a more verbal strategy in women compared to a stronger recruitment of spatial networks supporting a more spatial strategy use in men. In summary, this study provides evidence that different navigation strategies activate different brain areas in men and women.

Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility.

Community detection in social networks based on fire propagation

Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.

A serious challenge when finding influential actors in real-world social networks, to enable efficient community-wide interventions, is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework to discover effective network sampling heuristics by leveraging automatically learnt node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The output of this training is a transferable, adaptive policy that identifies an effective sequence of nodes to query on unseen graphs. The success of this policy is underpinned by a set of careful choices for embedding local and global information about the graph, and providing appropriate reward signals during training. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 7-23% improvement over the current state-of-the-art method.

PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or ‘teleport’ directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network.

The arterial baroreflex is a key mechanism for the homeostatic control of blood pressure (BP). In animals and humans, psychological stressors suppress the capacity of the arterial baroreflex to control short-term fluctuations in BP, reflected by reduced baroreflex sensitivity (BRS). While animal studies have characterized the brain systems that link stressor processing to BRS suppression, comparable human studies are lacking. Here, we measured beat-to-beat BP and heart rate (HR) in 97 adults who performed a multisource interference task that evoked changes in spontaneous BRS, which were quantified by a validated sequence method. The same 97 participants also performed the task during functional magnetic resonance imaging (fMRI) of brain activity. Across participants, task performance (i) increased BP and HR and (ii) reduced BRS. Analyses of fMRI data further demonstrated that a greater task-evoked reduction in BRS covaried with greater activity in brain systems important for central autonomic and cardiovascular control, particularly the cingulate cortex, insula, amygdala, and midbrain periaqueductal gray (PAG). Moreover, task performance increased the functional connectivity of a discrete area of the anterior insula with both the cingulate cortex and amygdala. In parallel, this same insula area showed increased task-evoked functional connectivity with midbrain PAG and pons. These novel findings provide human evidence for the brain systems presumptively involved in suppressing baroreflex functionality, with relevance for understanding the neurobiological mechanisms of stressor-related cardiovascular reactivity and associated risk for essential hypertension and atherosclerotic heart disease.

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we explore two community detection approaches based on the spectral partitioning to analyze a co-authorship network. The partitioning exploits the concepts of algebraic connectivity and characteristic valuation to form components useful for the analysis of relations and communities in real world social networks.