With online social networks (OSNs), people are exposed to tons of fake information or misleading posts. Celebrities sometimes intentionally create misleading posts in OSNs to guide people for commercial or marketing purposes. The intentional phrases from such posts can affect the online rating and even lead to a frenzy shopping. That’s part of the reasons that the top social media influencers are targeted by merchants to help promote products. The Federal Trade Commission (FTC) requires that all sponsored posts must be clearly disclosed. However, many influencers do not follow the FTC rules. As a result, people may be misled by the undisclosed sponsorship. In this study, for the first time, we explore the credibility of posts on Instagram and analyze if an influencer complies with the FTC requirements. We build an effective Undisclosed Sponsored Post Detection (USPD) framework based on an ensemble of machine learning classifiers. The USPD framework consists of three main processes: (i) feature extraction, (ii) model construction and (iii) credibility and integrity analysis. Our analysis and experiments demonstrate that the proposed framework can achieve a high accuracy of 83% for undisclosed sponsored post detection. The proposed framework also takes advantages of the text, user and image features in OSN posts to effectively analyze how much an influencer can be trusted.

The ultra-fast speed and massive capacity in 5G networks push huge amounts of data to networks. With network function virtualization, these data will go through multiple service functions (SFs) and big data processing/analysis. As a result, the processing delay from such SFs and data processing/analysis can significantly impact the delivery of latency-sensitive services. To reduce the processing delay, network function parallelism techniques are introduced to allow multiple SFs running parallelly for the same request. In this work, we study how to apply network function parallelism into SF chaining and embedding to optimize the latency. When physical nodes have unlimited computing resource, we propose the mixed integer programming based parallelism-aware SFC optimization (MIP-PS) algorithm. Our analysis proves the proposed MIP-PS is integer-approximation. When physical nodes have limited computing resource, we propose the latency factor based parallelism-aware SFC optimization (LF-PS) algorithm. Our extensive simulations demonstrate that our proposed schemes outperform the approaches extended directly from the existing work.

In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%.

The emerging multiaccess edge computing (MEC) architecture brings the needed computing resource to the network edge. Many 5G and Internet of Things (IoT) applications are latency sensitive and computation intensive in MEC systems. To flexibly provide and manage the network service requests in MEC systems, network function virtualization (NFV) can be employed to create a chain of service functions (SFs), namely, SF chain (SFC). Through SFC, the customer forwards user data to the edge server/cloud, and the edge server/cloud may return the processed results/models to the customer. When the forward and backward traffic is carrying different content, different SFs may be required for the forward and backward traffic, which requires a hybrid SFC (h-SFC). In this article, we study how to minimize the latency cost when embedding an h-SFC in MEC systems. We define a new problem called minimum latency hybrid SFC embedding (ML-HSFCE) and propose an algorithm, namely, optimal hybrid SFC embedding (Opt-HSFCE) to optimally embed a given h-SFC in MEC systems. Our extensive simulations and analysis show that the proposed Opt-HSFCE needs much less runtime compared with the brutal force algorithm and significantly outperforms the schemes that are directly extended from the existing techniques.

Network Function Virtualization (NFV) takes advantages of the emerging technologies in virtualization and automation to offer new ways in design, deployment, and management of networking services. In NFV, the proprietary hardware-based network functions are replaced by the software-based modules named as Virtual Network Functions (VNFs) or Service Functions (SFs). A network service request from the customer can be formed by multiple SFs. To satisfy a network service request, the service provider has to chain the SFs in the request into a Service Function Chain (SFC) and embed the constructed SFC onto the shared substrate network. In this paper, we comprehensively study how to composite and embed an SFC onto a shared substrate network with unique service function. We formulate this problem with the Integer Linear Programming (ILP) technique. We also propose an efficient heuristic algorithm with 2-approximation boundary, namely, Spanning Closed Walk based SFC Embedding (SCW-SFCE). Our extensive simulations and analysis show that the proposed approach can achieve near-optimal performance in a small network and outperform the Nearest Neighbour (NN) algorithm.

Measuring the similarity between nodes is challenging in social networks. The SimRank and CoSimRank are techniques widely used to calculate the similarity of two nodes in a social graph. They can be applied to many applications such as recommending friends and detecting communities in social networks. Both SimRank and CoSimRank are based on random walk and only consider first-order transition probabilities, in which the next node to visit in random walk solely depends on the current node, like a Markov chain. However, in many real-world situations, simply considering the current node may not be enough. Previously visited node may provide extra information for measuring similarities. In this paper, we propose a novel similarity measure technique by investigating CoSimRank to take advantage of the second-order information in a random walk process. Our extensive analysis and experiments show that the proposed second-order CoSimRank significantly outperforms the existing techniques.