An Adaptive System Supporting Collaborative Learning Based on a Location-Based Social Network and Semantic User Modeling

Disabled people can benefit greatly from assistive digital technologies. However, this increased human-machine symbiosis makes it important that systems are personalized and transparent to users. Existing work often uses data-oriented approaches. However, these approaches lack transparency and make it hard to influence the system’s behavior. In this paper, we use knowledge-based techniques for personalization, introducing the concept of Semantic User Models for representing the behavior, values and capabilities of users. To allow the system to construct such a user model, we investigate the use of a conversational agent which can elicit the relevant information from users through dialogue. A conversational interface is essential for our case study of navigation support for visually impaired people, but in general, has the potential to enhance transparency as users know what the system represents about them. For such a dialogue to be effective, it is crucial that the user understands what the conversational agent is asking, i.e., that misalignments that decrease the transparency are avoided or resolved. In this paper, we investigate whether we can use a conversational agent for Semantic User Model elicitation, which types of misalignments can occur in this process and how they are related, and how misalignments can be reduced. We investigate this in two (iterative) qualitative studies (n = 7 & n = 8) with visually impaired people in which a personalized user model for navigation support is elicited via a dialogue with a conversational agent. Our results show four hierarchically structured levels of human-agent misalignment. We identify several design solutions for reducing misalignments, which point to the need for restricting the generic user model to what is needed in the domain under consideration. With this research, we lay a foundation for conversational agents capable of eliciting Semantic User Models.

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.

In a rapidly changing environment, a shift toward decentralized structure for organizations seems inevitable. Semantic Web technologies combined with social network analysis make the transition of a centralized organization to a decentralized one much easier. By increasing controllability and manageability of a decentralized structure, these tools can improve functionality and performance. A framework for creation and maintenance of a semantic social network is introduced and two real world organizations have been analyzed. After selecting one organization suitable for decentralization, network datasets were generated from its structure. Generated datasets have been used as the input of the framework to create a semantic social network for the corresponding organization. Social network analysis was then performed on both semantic and non semantic social networks. Gain of performance was observed in many areas such as decision making, flexibility, reliability and throughput. Semantic social network also enables other functionalities along ease of decentralization such as gain of knowledge about dynamic structure of the organization. These functionalities are also discussed.

Semantic social network (SSN) is an extension of the current social network wherein meaningful data is added with each piece of information, known as metadata. This metadata is processed by machines to make decisions based on intelligent algorithms. SSN gives a virtual social circle where individuals can share information, ideas, news and stay connected with their friends and family. The semantic social information can be used to measure various behavioral traits of SSN individuals using metrics. There are several metrics developed to calculate the individual’s traits of the semantic social network. However, few metrics may produce wrong results in the absence of time consideration during their calculations. In this article, we have proposed a novel technique to improve the accuracy of activity factor metric calculation and another metric to calculate peak active time for an activity. The extracted results from our novel SSN metrics can help authorities to make better decisions in providing offers, rewards, suggestions, and badges for the SSN members as per their needs and interests.

With the proliferation of social network services (e.g., Facebook, Twitter, and Instagram), identifying cohesive subgraphs in networks is a key and fundamental problem to understand users' activity. Recently this problem has received extensive research attention. Identifying cohesive subgraph structures in networks has many applications such as recommendation, detecting fraudsters, event organization, and graph analysis. In this thesis, we study three important problems of finding cohesive subgraphs. Our studies include finding a query-based cohesive subgraph search in social networks and location-based social networks, and detecting all cohesive subgraphs in attributed bipartite networks. In our study of Density Modularity based Community Search (DMCS), we propose the modularity-based community search problem in networks. Modularity is a widely used measure of the structure of networks which checks the strength of partition of a network into communities. Most of the existing community search models only focus on the internal cohesiveness of a community. However, a high-quality community often has high modularity, which implies dense connections inside communities and sparse connections to the nodes outside the community. Hence, we conduct a pioneer study on searching for a community with high modularity. We point out that while modularity has been used in community detection (unrelated to query nodes), its application in community search (related to query nodes) brings in different challenges named free-rider effect and resolution limit problems. Both problems indicate that intrinsically optimizing the modularity can fail to find a small-sized community as a result for the community search problem. We mitigate these problems by designing a new graph modularity function named Density Modularity. To the best of our knowledge, this is the first work on the community search problem by using the graph modularity. The community search based on the density modularity, termed as DMCS, is to find a community in a social network that contains all the query nodes and has high density modularity. We prove that the DMCS problem is NP-hard, and thus there is no exact algorithm that is scalable to large graphs. To efficiently address DMCS, we present new algorithms that run in log-linear time to the graph size. We conduct extensive experimental studies in real-world and synthetic networks, which offer insights into the efficiency and effectiveness of our approximate algorithms. In our study of GeoSocial Community Search(GCS) problem, we propose the community search problem in location-based social networks. Specifically, we aim at finding a social community and a location cluster that are densely connected in a location-based social network. GCS can be useful for finding marketing targets and user/location recommendations. To the best of our knowledge, this is the first work to find a social community and a location cluster that are densely connected from location-based social networks. We prove that the GCS problem is NP-hard and is not in APX, unless P = NP. To solve GCS problem, we propose three effective and efficient algorithms: Core-based Basic Algorithm, Top-down Greedy Removing Algorithm, and Bottom-up Greedy Expansion Algorithms. Finally, we report extensive experimental studies that offer insights into the efficiency and effectiveness of the proposed solutions. In our study of Attributed Bipartite Co-clustering (ABC), we propose the co-clustering problem in attributed bipartite networks. Co-clustering in bipartite networks is a fundamental and important problem. Specifically, we unify two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. To the best of our knowledge, this is the first work to find a set of co-clusters while considering the attribute cohesiveness. We prove that ABC problem is NP-hard and propose three effective and efficient algorithms: (1) Top-Down Algorithm; (2) Bottom-Up Algorithm; and (3) Group-based Matching Algorithm. Finally, extensive experimental results on real-world attributed bipartite networks demonstrate the efficiency and effectiveness of our algorithms.

These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014. Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.

In the last two decades, the issue of Influence Maximization (IM) in traditional online social networks has been extensively studied since it was proposed. It is to find a seed set which has maximum influence spread under a specific network transmission model. However, in real life, the information can be spread not only through online social networks, but also between neighbors who are close to each other in the physical world. Location-Based Social Network (LBSN) is a new type of social network which is emerging increasingly nowadays. In a LBSN, users can not only make friends, but also share the events they participate in at different locations by checking in. In this paper, we aim to study the IM in LBSNs, where we consider both the influence of online and offline interactions. A two-layer network model and an information propagation model are proposed. Also, we formalize the IM problem in LBSNs and present an algorithm obtaining an approximation factor of \((1 - 1/e - \epsilon \)) in near-linear expected time. The experimental results show that the algorithm is efficient meanwhile offering strong theoretical guarantees.

Trust-aware location recommendation in location-based social networks: A graph-based approach

Location-based social network (LBSN) is a highly used social network at present. Detecting the community structures in LBSN plays a significant role in analyzing the network comprehensively. Modularity optimization is a traditional social network community detection method which has excellent performance. Whereas, resolution limit is an inherent problem of it. Some existing community detection methods for LBSN fail to take account of the full network information. To solve above problems, a network weighting scheme is proposed in this paper. Firstly, we combine a network embedding method that named walk2friends with this paper. In view of the shortcoming of walk2friends, we proposes a collaborative filtering method based on social relationship to improve it. Then, the learned similarity is obtained by improved walk2friends and the structural similarity is obtained by a random walk method. These two similarities are fused and then transformed into edge weight. Finally, the undirected unweighted graph is preprocessed as an undirected weighted graph. Experimental results on real networks show that when using modularity optimization method for optimization the weighting scheme not only can avoid the resolution limit but also applicable to LBSN . The mined community is more cohesive than the existing work on LBSN.

The construction of social networks over mobile devices in the events or at locations has emerged as a new network paradigm. These new mobile social networks enable people to communicate and share their experiences without the need to have Internet access and with minimum required infrastructure. However, the functionality and security of such networks would be potentially undermined without an effective trust management scheme. Although many trust management systems have been proposed, few of them can be applied to these new mobile social networks because of the unique network and communication characteristics. This paper presents a novel trust management system, termed MobileTrust, to establish decentralized, secure and reliable trust relationships between mobile ad hoc social network participants. Specifically, the construction of trust models encompasses both scenarios that users are experienced with the network and users are unacquainted with the environment. The trust models cover various important factors of trust relationships in social networks, including the similarity of user profile, reputation, and history of friendship. A set of simulations is conducted to evaluate our system deployed in a mobile social network in the presence of dishonest users.

Friend recommendation is an important real-world application in Location-based Social Networks (LBSN), helping users discover potential friends and enhance their overall happiness. LBSN mainly comprises two distinct data structures: spatio-temporal data for human mobility and graph data for social networks. These two data structures make it challenging to model the complex relationships between them, which are essential for comprehensively understanding users’ lives. Previous studies have either modeled user trajectories and social networks separately or used classical simple graph-based methods, where a simple edge links only two nodes, failing to capture the multiple relationships inherent in LBSN. Furthermore, most studies have relied on Euclidean space to train their graph models, which could result in significant distortion because of tree-like social network data structure. To address these limitations, we propose a novel heterogeneous LBSN hypergraph that represents user check-in records and continuous trajectories—comprising multiple Points of Interest (POI)—as hyperedges, enabling the representation of complex spatio-temporal relationships. This approach enables us to link multiple nodes of different types by hyperedges and use hyperbolic spaces to create more efficient graph representations. Additionally, we devise a new type-specific attention mechanism for our Heterogeneous Hyperbolic Hypergraph Neural Network (H 3 GNN), which is end-to-end trainable and employs supervised contrastive learning to learn hypergraph node embeddings for the subsequent friend recommendation task with the help of hyperbolic space. Finally, our model H 3 GNN achieves better results than existing methods on six real-world city datasets, and our ablation studies demonstrate the effectiveness of each component. Additionally, our experiments indicate that H 3 GNN requires less data storage and training time compared to previous methods.

We demonstrate SSNetViz that is developed for integrating, visualizing and querying heterogeneous semantic social networks obtained from multiple information sources. A semantic social network refers to a social network graph with multi-typed nodes and links. We demonstrate various innovative features of SSNetViz with social networks from three information sources covering a similar set of entities and relationships in terrorism domain.

Much remains unknown about moment-to-moment social-network cognition—that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated—and which are neglected—through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

One of the most attractive problems of social network analysis is the link prediction. Social networks' user growth is mostly supported with data driven friend recommendations which are provided by link predictors. Previously, we had studied new features to improve prediction accuracy in Location Based Social Networks (LBSNs) where users share temporal location information with check-in interactions. In this paper, we focused on the efficiency of link predictors as the speed of prediction is as critical as its accuracy in LBSNs. Extraction time costs and prediction accuracy of individual LBSN features are mined to pick a feature subset that is achieving faster link prediction while not losing from accuracy.

This paper aims to construct a social network among e-learning learners on the basis of the location information of the learner and the instructor obtained using GPS sensors, etc. For this, we suggest a system that supports the construction of a social education network service using the location information of the smart-phone in e-learning. This system provides a mechanism to form a social network service among students who take the same on-line course or have similar interests. Through this system, e-learners can create communities for learning and exchange help among them. The support of a location based social network service in our system would increase interactions among e-learners and improve satisfaction regarding their mobile learning environment.