---
title: 'HeroCon: Citation Network Diagnostic'
url: https://www.emergentmind.com/topics/herocon
type: topic
---

# HeroCon: Citation Network Diagnostic

Searching arXiv for the referenced papers and term variants to ground the article.
In the provided arXiv literature, HeroCon most directly corresponds to **HEROCON**, the **Holistic Equilibrated Research Outreach CONstellation score** introduced with **Citation-Constellation**, a **free, open-source, no-code, and auditable tool for citation network decomposition**. HEROCON is an author-level bibliometric score that assigns **graduated weights** to citations according to the **network proximity between citing and cited authors**, thereby decomposing a citation profile by social and institutional structure rather than treating all citations as equivalent. It is presented together with **BARON** as a **structural diagnostic**, not a quality indicator, and its default weights are explicitly described as **experimental** and in need of empirical calibration [2603.24216].

## 1. Conceptual definition and motivation

HEROCON is defined against a specific critique of standard bibliometrics: **standard citation metrics treat all citations as equal, obscuring the social and structural pathways through which scholarly influence propagates**. In response, Citation-Constellation asks where citations come from within the social graph surrounding a focal researcher, rather than only how many citations exist in aggregate [2603.24216].

The paired metric **BARON** is a **strict binary metric** that counts only citations from **outside** the detected collaborative and institutional network. HEROCON, by contrast, is a **graduated, weighted metric** that gives **partial credit** to in-group citations according to relationship proximity. In the terminology of the paper, HEROCON is “holistic” because it represents the full pattern of citation origins across self, co-author, institutional, venue-governance, and external layers, and “equilibrated” because it down-weights structurally likely in-group citations rather than discarding them entirely [2603.24216].

This design places HEROCON between raw citation totals and strict external-only counting. A self-citation receives no credit; a citation from a direct co-author receives limited credit; a citation from a more distant relation receives more; and a citation with **no detected relationship** receives full credit. The resulting score is intended to describe **where in the social graph citations originate**, not whether the underlying research is of high or low quality [2603.24216].

## 2. Network layers, weighting scheme, and score construction

HEROCON operationalizes citation origin through **concentric layers** of proximity between the citing author(s) and the focal researcher. The paper distinguishes the following classes: **Layer 0 – Self**, **Layer 1 – Direct co-author**, **Layer 2 – Transitive co-author**, **Layer 3 – Institutional colleagues (temporal)**, **Layer 4 – Venue governance relationships**, and **Layer 5 – External** [2603.24216].

BARON is binary: all in-group citations receive weight 0, and only **EXTERNAL** citations receive weight 1. HEROCON instead assigns the following default weights.

| Layer / class | Relationship | HEROCON weight |
|---|---|---:|
| 0 | Self-citation | 0.0 |
| 1 | Direct co-author | 0.2 |
| 2 | Transitive co-author | 0.5 |
| 3a | Same department | 0.1 |
| 3b | Same institution, different department | 0.4 |
| 3c | Same parent organization | 0.7 |
| 4a | Venue self-governance | 0.05 |
| 4b | Venue editor is co-author | 0.15 |
| 4c | Venue editor at same institution | 0.3 |
| 4d | Committee member in network | 0.4 |
| 5 | External | 1.0 |

The score is computed over **classifiable citations**, excluding **UNKNOWN** cases. In prose, BARON is the percentage of classifiable citations that are external, whereas HEROCON is the percentage obtained by summing citation weights and dividing by classifiable citations. The paper also defines a **diagnostic gap** as:

$$
\text{Diagnostic gap} = \text{HEROCON} - \text{BARON}
$$

This gap is interpreted as the contribution of **in-group citations** under the current weighting scheme. The weights are **configuration-dependent**, can be overridden through a JSON configuration, and are explicitly described as a **testable hypothesis** rather than a finalized empirical standard [2603.24216].

## 3. Graph-theoretic and temporal classification procedure

The classification pipeline begins from a co-authorship graph. For every pair of authors \( (a,b) \) who co-authored a paper, the tool assigns an edge strength

$$
\text{strength}(a,b) =
\text{shared\_papers}(a,b)\times e^{-0.1 \cdot \text{years\_since\_last\_collaboration}}
$$

with the exponential factor yielding a **half-life of about 7 years**. The paper states, however, that this recency weighting currently **modulates metadata in the audit trail** rather than altering BARON/HEROCON decisions, which are based on **graph distance** [2603.24216].

A **Breadth-First Search (BFS)** from the focal researcher to configurable depth \(d\) determines co-author classes. With default depth 2, distance 0 maps to **SELF**, distance 1 to **DIRECT\_COAUTHOR**, and distance 2 to **TRANSITIVE\_COAUTHOR**. Authors beyond depth \(d\) are not counted as part of the co-author network and can subsequently be classified through institutional or venue-governance relations instead [2603.24216].

Institutional proximity is handled temporally. The tool constructs a **time-stamped affiliation timeline** for the focal researcher and compares citing authors’ affiliations at the year of the citing work using **ROR IDs and parent hierarchies**. This yields **SAME\_DEPT**, **SAME\_INSTITUTION**, or **SAME\_PARENT\_ORG** where appropriate. If neither co-authorship nor institutional overlap is detected, the system proceeds to venue-governance checks; if no relation is found there either, the citation is classified as **EXTERNAL** [2603.24216].

The decision order is explicit: self-citation, then co-author radius, then temporal institutional overlap, then venue governance, then external. This procedural ordering matters because HEROCON is a **per-citation decomposition**: each citation is assigned a specific structural rationale rather than only contributing to an aggregate score [2603.24216].

## 4. Data sources, identity resolution, and UNKNOWN handling

Citation-Constellation relies on **OpenAlex** as the main source of works, authors, citation links, affiliations, and venues; **ORCID** as a trust anchor for focal-author identity and work validation; and **ROR** for institution matching and parent-organization hierarchy [2603.24216].

Identity resolution is treated as a central methodological constraint. When the focal author has **high ORCID coverage**—stated as **≥ ~70% of their works**—the tool trusts only works appearing in both ORCID and OpenAlex for network construction and scoring. When ORCID coverage is low, OpenAlex works are provisionally retained, but the system performs **affiliation anomaly detection** against the known institutional timeline and flags potential mismatches for manual review. Manual confirmation decisions are logged in the audit trail [2603.24216].

The category **UNKNOWN** addresses incomplete metadata. Typical cases include missing focal-author affiliations at the relevant time or missing affiliations for all citing authors on a citation. The paper emphasizes that UNKNOWN citations are **excluded from the denominator** of both BARON and HEROCON. This avoids silently treating unknowns as external and artificially inflating external-reach scores, but the paper also notes that this introduces a **known selection bias** and identifies sensitivity analyses on UNKNOWN handling as a priority for future work [2603.24216].

A plausible implication is that HEROCON is not merely a weighting scheme but a metadata-quality-sensitive audit instrument. The score depends on author disambiguation, temporal affiliation coverage, and venue metadata; accordingly, the tool reports **data quality statistics**, including the percentage of classifiable citations and **reliability labels such as HIGH/MODERATE/LOW** [2603.24216].

## 5. Phased architecture, software implementation, and auditability

The software implements a **four-phase pipeline**. **Phase 1** performs **self-citation analysis** and yields a rudimentary \(\text{BARON}_{v0.1}\) based on **NON\_SELF** citations. **Phase 2** constructs the co-author graph, introduces HEROCON, and distinguishes **SELF**, **DIRECT\_COAUTHOR**, **TRANSITIVE\_COAUTHOR**, and **EXTERNAL**. **Phase 3** performs **temporal institutional affiliation matching via ROR** and introduces **SAME\_DEPT**, **SAME\_INSTITUTION**, **SAME\_PARENT\_ORG**, and **UNKNOWN**. **Phase 4**, still **under active development**, adds **AI-agent-driven venue governance extraction** [2603.24216].

The abstract states that **Phases 1–3 are fully operational; Phase 4 is under development**. For Phase 4, the paper describes a governance-database construction step that uses a **local LLM**—specified as **Qwen 3.5 8B, quantized, served via llama.cpp**—to parse venue HTML and extract editorial or committee membership, followed by citation reclassification into governance-linked categories such as **VENUE\_SELF\_GOVERNANCE**, **VENUE\_EDITOR\_COAUTHOR**, **VENUE\_EDITOR\_AFFIL**, and **VENUE\_COMMITTEE** [2603.24216].

The tool is available both as a **no-code web interface** and as a **Python CLI**. The web workflow supports ORCID or OpenAlex author IDs, configurable co-author graph depth, optional manual validation of flagged works, and custom HEROCON weight files in JSON. Typical execution time is stated as **1–4 minutes for 50–100 papers**. Each run produces a detailed **audit JSON** containing included and excluded works, per-citation classifications, the assigning phase, confidence levels, human-readable rationales, network and affiliation snapshots, summary counts, and BARON/HEROCON outputs [2603.24216].

This audit orientation is central to the concept. The paper characterizes Citation-Constellation as **auditable**, and every classification decision is intended to be **inspectable evidence** rather than opaque score production. In methodological terms, HEROCON is therefore coupled to a provenance system rather than presented as a standalone scalar [2603.24216].

## 6. Interpretation, intended use, and relation to other “HERO” usages

HEROCON is explicitly framed as a **structural diagnostic**. The paper proposes qualitative reading of the **HEROCON − BARON** gap: **small gap (e.g., < 3%)**, **moderate gap (3–10%)**, and **large gap (> 10%)**. These ranges are presented as interpretive guidance about **inner-circle dependence**, while the text simultaneously emphasizes that such patterns are not inherently good or bad and may reflect field size, collaboration structure, or institutional context [2603.24216].

The intended uses are **self-auditing / self-reflection**, **science-of-science and policy research**, and contextualization in evaluation only **with strong caveats**. The paper is unambiguous that **BARON and HEROCON are not quality metrics** and **must not be used as stand-alone criteria in hiring, promotion, tenure, or funding decisions**. They cannot distinguish healthy collaboration from gaming, small-field structure from insularity, or merit-based citation from network-mediated citation; they describe only **where citations originate in the social graph** [2603.24216].

Within bibliometrics, HEROCON differs from the **h-index**, **g-index**, total citations, and field-normalized indicators because those aggregate counts do not decompose citations by **social/network context**. It also differs from general network centrality measures and from tools such as **VOSviewer**, **CiteSpace**, **Bibliometrix**, and **Scite.ai**, since Citation-Constellation aims at an **individual-level decomposition of incoming citations by network proximity** plus an auditable two-score summary [2603.24216].

The term **HeroCon** is potentially ambiguous because two unrelated arXiv papers use the acronym **HERO** rather than **HEROCON**. One introduces **“HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization”**, where the paper explicitly states that **there is no explicit “HeroCon” in the paper**; the other introduces **“HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation”**, where “HeroCon” is presented only as a cue for discussing agentic systems rather than as a defined technical term [2510.09010; 2606.11559]. In the literature at hand, therefore, **HEROCON** is the formally defined concept associated with the name HeroCon, whereas the two HERO systems concern, respectively, **hardware-aware NeRF quantization** and **hindsight-enhanced self-distillation for multi-turn agents**, not citation-network diagnostics [2510.09010; 2606.11559].

Source: https://www.emergentmind.com/topics/herocon