About ERG Social
ERG Social helps you connect and share with the people in your life.

About Social Network

Currently, Facebook is the world’s greatest social platform. It has been praised and known as one of the best platforms that promote business growth and the exchange of ideas. Nevertheless, there are still some issues with. The major shortcoming with this platform is the many “Trash Information” that is displayed on its Newsfeed section. Indeed, necessity is the mother of all inventions. The ERG social network’s solution is, therefore, to build a platform that people can live a “Healthy Newsfeed”. This implies that they will identify their compelling and valuable data in their own newsfeed.

A practical example of how the ERG social network solves these challenges. In case you are a software engineer, the ERG social network will allow you to view a lot of important headlines and updated information that is relevant to your job. With that, you will notice a number of impressive recruitment news from various companies in the globe. Apart from this, you will see lots of news relating to your engineering specialty. In addition to that, you can make colleagues with others in the software engineering feld too. This way, ERG intends to build a platform that people can relate together much more than as in Facebook.

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 Understanding the ERG social network

The ERG social network is the primary decentralized platform administered by a society of peer to make use of cryptocurrency, influencer marketing and transactions based on blockchain to allow real end-to-end trade between businesses, people and machines. Interestingly, this is meant to happen without high-priced intermediaries. ERG’s social network is meant to meet users’ demands. Consequently, it builds a social network that is harmonious with the users’ preferences, age, social and economic activities, goals and much more. One of the prominent functions of the ERG social network is that people can earn by just uploading their individual content or video

 Privacy

Actually, the Eagle Rock Global (ERG) came into being due to the need for privacy that clients keep expressing. The ERG’s objective is to honor and satisfy that demand by providing the high-grade social experience, yet giving users the chance to control their account and data privacy. No doubt, in this current digital world, the privacy of data and account is paramount and cannot be ignored. With that in mind, ERG is in line with technological best practices.

 Opt-In Advertising

Normally, all advertising will need positive consent and willful participation. The Opt-In advertising will make things as simple as ABC for the user. This feature will be there in the Public Feed marketplace sections as well as for business accounts that any user prefers to take part in when they utilize ERG Social channels.

 The self-elimination of posts

Another basic functionality of secrecy or privacy is the capacities to have posts eliminate themselves from the ERG service at a given point in time. With this functionality, it does not matter whether a post was privately posted or publicly posted. These messages will remain for a planned period after which a self-destruction sequence is triggered to remove all of the messages from all intercommunications. This kind of timed activities has now become quite popular in existing social media simply because they enable users to share an experience or tell a story in a distinct manner. In the platform, followers observe these events to have a different insight into celebrities and friends, building exclusivity since the succeeding fans cannot view that shared and destroyed.

 GPS: For fnding buddies and public users

In this current age, mobile devices and Smartphones have had some useful features that can be utilized as well. For example, some include a number of sensors that will allow users to obtain and open to promoting the growth of their community. The Global Positioning System (GPS) is the greatest option since it will enable users to switch on the feature and sensor and subsequently located others in their region. ERG plans to facilitate multiple location-based contexts. This includes enabling friends to discover who is near their location, supporting group rallies with fans and followers, or users deciding to open up data to the public to look for friends who stay near them.

From this, it is quite clear that the ERG social network is an effcient and secure platform that intends to improve the user experience. Assuredly, it enhances the features that other social network platforms have put in place and further introduces other interesting points that even make it a unique platform.

Micro level

At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context.

Dyadic level

A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality.

Triadic level

Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality.In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs.

Actor level

The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges.Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.

Subset level

Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior.

Meso level

n general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.

Meso level

In general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.

Organizations

Formal organizations are social groups that distribute tasks for a collective goal.Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a workgroup level and organization level, focusing on the interplay between the two structures.Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.

Randomly distributed networks

Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general degree-based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node-level, homophily and attribute-based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties. Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.

Scale-free networks

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups.Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of vertices with a degree that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This distribution also follows a power law.The Barabási model of network evolution shown above is an example of a scale-free network.

Macro level

Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping).

Complex networks

Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure (see stochastic block model), and hierarchical structure. In the case of agency-directed networks these features also include reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features.

 Social media

Computer networks combined with social networking software produces a new medium for social interaction[67]. A relationship over a computerized social networking service can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a computer mediated communication context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the "real" world. Social network analysis methods have become essential to examining these types of computer mediated communication.

In addition, the sheer size and the volatile nature of social media has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.