Self organizing maps (SOMs) or cognitive maps are designed to preserve major topological attributes of manifolds in a higher dimensionality data space to corresponding projections thereof in a low dimensional coordinate space. This is performed by mapping neighborhoods and the distances contained therein from the data space to ones in the coordinate space. Thus, SOM functionality relies heavily on the geometrical properties of both spaces. Topologically flexible data space distance metrics are constructed by combining tensors with graph filters, the latter coming from graph signal processing. The power of these distance metrics comes from naturally expressing the higher order relationships between points of the data space. This paves the way for addressing engineering scenarios involving a large number of densely interrelated attributes. One such case is discerning personalities from textual information based on the Myers-Briggs taxonomy indicator (MBTI), a framework of archetypal personalities derived from Jungian psychodynamic theory. Various graph filters were tested on a benchmark Kaggle dataset with ground truth with comparisons assessed in terms of topological error, cluster purity, average inter-cluster distance, and cluster curvature variability. Data points were stored in Pinecone, a recent vector database, with Python integration.

Matrix factorization is paramount in large scale graph mining as well as a versatile paradigm for dimensionality reduction. In particular, factoring a graph adjacency matrix may well reveal, depending on the specific f actor properties, higher order structure. The latter describes global graph properties better compared to first order connectivity patterns such as vertex degrees. The Boolean semiring factorization of an adjacency matrix yields a product of two smaller and sparser matrices where the former contains disjoint fundamental vertex subsets and the latter combinations thereof. Therefore, the first factor represents community structure and the second has the cross connections between them. In this way graph partitioning and dimensionality reduction are simultaneously achieved. Because of the nature of the Boolean semiring, most common linear algebraic solvers cannot be applied. Moreover, the exact factorization is NP hard. To address these limitations, a genetic algorithm has been developed with evolutionary operations tailored to heuristically compute said factorization which offers interpretability and a high parallelism potential. Besides graph mining the major applications of the Boolean semiring factorization include role mining in enterprise database and operating system realms, curriculum design, and graph flows under inflexible uniqueness constraints. The results obtained by applying the proposed genetic algorithm to synthetic graph benchmarks are very encouraging.

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.

At the dawn of the Internet era graph analytics play an important role in high- and low-level network policymaking across a wide array of fields so diverse as transportation network design, supply chain engineering and logistics, social media analysis, and computer communication networks, to name just a few. This can be attributed not only to the size of the original graph but also to the nature of the problem parameters. For instance, algorithmic solutions depend heavily on the approximation criterion selection. Moreover, iterative or heuristic solutions are often sought as it is a high dimensional problem given the high number of vertices and edges involved as well as their complex interaction. Replacing under constraints a directed graph with an undirected one having the same vertex set is often sought in applications such as data visualization, community structure discovery, and connection-based vertex centrality metrics. Polar decomposition is a key matrix factorization which represents a matrix as a product of a symmetric positive (semi)definite factor and an orthogonal one. The former can be an undirected approximation of the original adjacency matrix. The proposed graph approximation has been tested with three Twitter graphs with encouraging results with respect to density, Fiedler number, and certain vertex centrality metrics based on matrix power series. The dataset was hosted in an online MongoDB instance.

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.