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This paper deals with the analysis of limit cycle oscillations in H-bridge wireless power transfer resonant inverters under primary-side Zero Current Switching (ZCS) control. The limit cycles are computed by solving their initial value problem. If this problem is not dealt with properly, erroneous results may be derived and ghost or physically inadmissible limit cycles may be obtained. A complementary condition must be added to obtain only real limit cycles that can take place in the system. The stability analysis of these real cycles is performed using Floquet theory. For the case of the series-series compensated topology, the resulting monodromy matrix reveals that these cycles are stable whenever they exist and the load resistance is larger than a critical value. On the contrary, for load resistance smaller than this critical value, coexistence of different real stable limit cycles is also possible. While one of the limit cycles always exists for the whole range of load resistance values, two of them are created/destroyed through a cyclic fold bifurcation. The boundary of this bifurcation is determined. Numerical simulations corroborate the theoretical predictions and some experimental measurements are presented to validate some of the theoretical and simulation results. [ABSTRACT FROM AUTHOR]

Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only considers a single kind of interaction, so they cannot deal with various kinds of interactions on multi-view attributed networks. It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks. In this paper, we propose a graph convolution-based framework, named AnomMAN , to detect Anom aly on M ulti-view A ttributed N etworks. To jointly consider attributes and all kinds of interactions on multi-view attributed networks, we use the attention mechanism to define the importance of all views in networks. Since the low-pass characteristic of graph convolution operation filters out most high-frequency signals (abnormal signals), it cannot be directly applied to anomaly detection tasks. AnomMAN introduces the graph auto-encoder module to turn the disadvantage of low-pass features into an advantage. According to experiments on real-world datasets, AnomMAN outperforms the state-of-the-art models and two variants of our proposed model. [ABSTRACT FROM AUTHOR]

Complex systems are composed of multiple types of interactions, where each type of interaction is encoded in a layer, resulting in multi-layer networks. Detecting layer-specific modules in multi-layer networks are for revealing the functions and structure of systems. However, current algorithms are criticized for failing to quantify and balance the specificity and connectivity of communities in multi-layer networks, resulting in undesirable performance. To address these problems, we propose a joint L earning S pecific and C onserved features for C lustering in multi-layer networks (called LSCC), where features of vertices simultaneously characterize the shared and layer-specific structure of networks. Specifically, LSCC jointly factorizes multi-layer networks by projecting all layers into a common subspace with nonnegative matrix factorization, where the structure of various layers is represented. Then, LSCC decomposes features of vertices into the conserved and specific parts, where the specificity of vertices of each layer is explicitly quantified. To balance the specificity and connectivity of modules, LSCC joint learns feature extraction and subspace clustering, which is formulated as an optimization problem. The experimental results on 8 datasets demonstrate that the proposed algorithm significantly outperforms the baselines on various measurements. [ABSTRACT FROM AUTHOR]

Complex systems are composed of multiple types of interactions, where each type of interaction is encoded in a layer, resulting in multi-layer networks. Detecting layer-specific modules in multi-layer networks are for revealing the functions and structure of systems. However, current algorithms are criticized for failing to quantify and balance the specificity and connectivity of communities in multi-layer networks, resulting in undesirable performance. To address these problems, we propose a joint L earning S pecific and C onserved features for C lustering in multi-layer networks (called LSCC), where features of vertices simultaneously characterize the shared and layer-specific structure of networks. Specifically, LSCC jointly factorizes multi-layer networks by projecting all layers into a common subspace with nonnegative matrix factorization, where the structure of various layers is represented. Then, LSCC decomposes features of vertices into the conserved and specific parts, where the specificity of vertices of each layer is explicitly quantified. To balance the specificity and connectivity of modules, LSCC joint learns feature extraction and subspace clustering, which is formulated as an optimization problem. The experimental results on 8 datasets demonstrate that the proposed algorithm significantly outperforms the baselines on various measurements. [ABSTRACT FROM AUTHOR]

Location-based Social Networks (LBSN) such as Foursquare have become ubiquitous due to the vast spread of smart mobile devices. The users of LBSN can check in at any locations, write tips, share comments, etc. LBSN has attracted much research attention due to the fact that the large volume of check-ins provides an unprecedented channel to connect users’ online world to their offline physical activities. An essential analysis task is to identify hidden community from these check-ins, i.e., the community not explicitly defined based on the online links in LBSN but implicitly exist in the mobility patterns from these check-ins. The current state-of-the-art systems explore user check-ins to extract the visited venues; analyze the semantic information of the visited venues to model user behaviour patterns; cluster the users with similar patterns into the same community. They assume that users’ check-ins specify the exact venues they visited. However, it has been observed that more than half of users’ check-ins, e.g., in Twitter and Foursquare , are venueless ( i.e., exact venues visited are unknown). Motivated by this observation, this work as the first attempt investigates the hidden community detection by exploring all check-ins ( i.e., check-ins with/without exact venues). The idea is to identify a list of nearby venues around venueless check-ins. To obtain unfailing regularity even though it is uncertain on which venues have been eventually visited, we develop methods based on the probabilistic venue sequences. By tackling a number of unique technical challenges, the rich and comprehensive information disclosed from all check-ins allows us to uncover communities with high modularity which cannot be achieved by state-of-the-art systems. Empirical study conducted on both real-world and synthetic datasets show the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]